Methods of predicting and monitoring tyrosine kinase inhibitor therapy

ABSTRACT

The present invention provides methods for analyzing a combination of biomarkers to individualize tyrosine kinase inhibitor therapy in patients who have been diagnosed with cancer. In particular, the assay methods of the present invention are useful for predicting, identifying, or monitoring the response of a tumor, tumor cell, or patient to treatment with a tyrosine kinase inhibitor using an algorithm based upon biomarker profiling. The assay methods of the present invention are also useful for predicting whether a patient has a risk of developing toxicity or resistance to treatment with a tyrosine kinase inhibitor. In addition, the assay methods of the present invention are useful for monitoring tyrosine kinase inhibitor therapy in a patient receiving the drug to evaluate whether the patient will develop resistance to the drug. Furthermore, the assay methods of the present invention are useful for optimizing the dose of a tyrosine kinase inhibitor in a patient receiving the drug to achieve therapeutic efficacy and/or reduce toxic side-effects.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.60/783,743, filed Mar. 17, 2006, and U.S. Provisional Application No.60/829,812, filed Oct. 17, 2006, the disclosures of which are herebyincorporated by reference in their entirety for all purposes.

BACKGROUND OF THE INVENTION

Tyrosine kinases are a class of enzymes that catalyze the transfer ofthe terminal phosphate of adenosine triphosphate (ATP) to tyrosineresidues in protein substrates. Tyrosine kinases are believed, by way ofsubstrate phosphorylation, to play critical roles in signal transductionfor a number of cell functions. In fact, tyrosine kinases have beenshown to be important contributing factors in cell proliferation,carcinogenesis, and cell differentiation. Tyrosine kinases can becategorized as either receptor tyrosine kinases or non-receptor tyrosinekinases.

Receptor tyrosine kinases are key regulators of intercellularcommunication that controls cell growth, proliferation, differentiation,survival, and metabolism. About 20 different receptor tyrosine kinasefamilies have been identified that share a similar structure, namely anextracellular binding site for ligands, a transmembrane region, and anintracellular tyrosine kinase domain (see, e.g., Ullrich et al., Cell,61:203-212 (1990); Pawson, Eur. J. Cancer, 38(Supp 5):S3-S10 (2002)).For example, the EGFR family of receptor tyrosine kinases comprisesEGFR/HER1/ErbB1, HER2/Neu/ErbB2, HER3/ErbB3, and HER4/ErbB4. Ligands ofthis family of receptors include epithelial growth factor (EGF), TGF-α,amphiregulin, HB-EGF, betacellulin, and heregulin. Other receptortyrosine kinase families include the PDGF family, the FLK family, andthe insulin family of receptors.

Extracellular ligand binding of receptor tyrosine kinases induces orstabilizes receptor dimerization, leading to increased kinase activity.The intracellular catalytic domain displays the highest level ofconservation among receptor tyrosine kinases, includes the ATP-bindingsite that catalyzes receptor autophosphorylation of cytoplasmic tyrosineresidues, and serves as the docking site for Src homology 2 (SH2)- andphosphotyrosine-binding (PTB) domain-containing proteins such as Grb2,Shc, Src, Cb1, and phospholipase C-γ. These proteins subsequentlyrecruit additional effectors containing SH2-, SH3-, PTB-, andpleckstrin-homology (PH) domains to the activated receptor, whichresults in the assembly of signaling complexes at the membrane and theactivation of a cascade of intracellular biochemical signals. The mostimportant downstream signaling cascades activated by receptor tyrosinekinases include the Ras/Raf/mitogen activated protein (MAP) kinasepathway, the phosphoinositide 3-kinase/Akt pathway, and the JAK/STATpathway. The complex signaling network triggered by receptor tyrosinekinases eventually leads either to activation or repression of varioussubsets of genes and thus defines the biological response to a givensignal.

The activity of receptor tyrosine kinases and their signaling cascadesis precisely coordinated and tightly controlled in normal cells.However, deregulation of the receptor tyrosine kinase signaling system,either by stimulation through growth factor and/or through geneticalteration, produces deregulated tyrosine kinase activity. Theseaberrations generally result in receptor tyrosine kinases withconstitutive or strongly enhanced kinase activity and subsequentsignaling capacity, which leads to malignant transformation. Therefore,they are frequently linked to human cancer and also to otherhyperproliferative diseases such as psoriasis (Robertson et al., TrendsGenet., 16:265-271 (2000)). The most important mechanisms leading toconstitutive receptor tyrosine kinase signaling include overexpressionand/or gene amplification, genetic alterations such as deletions andmutations within the extracellular domain or catalytic site, andautocrine-paracrine stimulation through aberrant growth factor loops.

More particularly, gene amplification and/or overexpression of receptortyrosine kinases occurs in many human cancers, which might increase theresponse of cancer cells to normal growth factor levels. Additionally,overexpression of a specific receptor tyrosine kinase on the cellsurface increases the incidence of receptor dimerization, even in theabsence of an activating ligand. In many cases, this results inconstitutive activation of the receptor tyrosine kinase, leading toaberrant and uncontrolled cell proliferation and tumor formation. Forexample, EGFR/HER1/ErbB1 is frequently overexpressed in non-small celllung, bladder, cervical, ovarian, kidney, and pancreatic cancer as wellas in squamous cell carcinomas of the head and neck (Hong et al., Oncol.Biother., 1:1-29 (2000)). The predominant mechanism leading to EGFRoverexpression is gene amplification, with up to about 60 copies percell reported in certain tumors (Libermann et al., Nature, 313:144-147(1985)). In general, elevated levels of EGFR expression are associatedwith high metastatic rate and increased tumor proliferation (Pavelic etal., Anticancer Res., 13:1133-1138 (1993)). Therefore, receptor tyrosinekinases such as EGFR are recognized as attractive targets for the designand development of compounds that can specifically inhibit theirtyrosine kinase activity in cancer cells.

Small molecule tyrosine kinase inhibitors compete with the ATP-bindingsite of the catalytic domain of target tyrosine kinases. Such inhibitorsare generally orally active and have a favorable safety profile that caneasily be combined with other forms of cancer therapy. Several tyrosinekinase inhibitors have been identified to possess effective antitumoractivity and have been approved or are in clinical trials. These includegefitinib (Iress®), sunitinib (Sutent®; SU11248), erlotinib (Tarceva®;OSI-1774), lapatinib (GW-572016), canertinib (CI 1033), semaxinib(SU5416), vatalanib (PTK787/ZK222584), sorafenib (BAY 43-9006), imatinibmesylate (Gleevec®; STI571), and leflunomide (SU101). Although tyrosinekinase inhibitors represent a new class of targeted therapy thatinterferes with specific cell signaling pathways and allowstarget-specific therapy for selected malignancies, there is currently alack of tumor response to these inhibitors in the general population.For example, only about 10% of patients with non-small cell lung cancerin whom standard therapy failed respond to the EGFR inhibitor gefitinib(Fukuoka et al., J. Oncol., 21:2237-2246 (2003); Kris et al., JAMA,290:2149-2158 (2003)). In addition, patients may be at risk of toxicityto tyrosine kinase inhibitors. Furthermore, tyrosine kinase inhibitortherapy is typically very expensive in comparison to conventionalchemotherapy. Moreover, resistance to tyrosine kinase inhibitors canmanifest during treatment, and sometimes a particular inhibitor becomeswholly ineffective in certain patients.

As a result, due to the high cost of tyrosine kinase inhibitor therapy,the small percentage of responders, the risk of toxic side-effects, andthe possibility of developing resistance during treatment, it isimperative to prescribe tyrosine kinase inhibitors only to thosepatients for whom such therapy will have some benefit. Thus, there is aneed in the art for methods that utilize a combination of biomarkers topredict a patient's response to tyrosine kinase inhibitors such as EGFRinhibitors. There is also a need in the art for methods that utilize acombination of biomarkers to identify patients who are at greater riskof developing toxicity to tyrosine kinase inhibitors and to reduce thetoxic effects of tyrosine kinase inhibitors in patients alreadyreceiving the drug. There is a further need in the art for methods thatutilize a combination of biomarkers to identify patients with acquiredresistance to tyrosine kinase inhibitor therapy in recurring tumors. Thepresent invention satisfies these needs and provides related advantagesas well.

BRIEF SUMMARY OF THE INVENTION

The present invention provides methods for analyzing a combination ofbiomarkers to individualize tyrosine kinase inhibitor therapy inpatients who have been diagnosed with cancer. In particular, the assaymethods of the present invention are useful for predicting, identifying,or monitoring the response of a tumor, tumor cell, or patient totreatment with a tyrosine kinase inhibitor using an algorithm based uponbiomarker profiling. The assay methods of the present invention are alsouseful for predicting whether a patient has a risk of developingtoxicity or resistance to treatment with a tyrosine kinase inhibitor. Inaddition, the assay methods of the present invention are useful formonitoring tyrosine kinase inhibitor therapy in a patient receiving thedrug to evaluate whether the patient will develop resistance to thedrug. Furthermore, the assay methods of the present invention are usefulfor optimizing the dose of a tyrosine kinase inhibitor in a patientreceiving the drug to achieve therapeutic efficacy and/or reduce toxicside-effects.

In one aspect, the present invention provides an assay method foridentifying the response of a tumor to treatment with a tyrosine kinaseinhibitor, the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from a subject; and    -   (b) identifying the tumor as responsive or non-responsive to        treatment with the tyrosine kinase inhibitor using an algorithm        based upon the at least one profile.

In another aspect, the present invention provides an assay method forpredicting the response of a subject to treatment with a tyrosine kinaseinhibitor, the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from the subject; and    -   (b) predicting the likelihood that the subject will respond to        treatment with the tyrosine kinase inhibitor using an algorithm        based upon the at least one profile.

In yet another aspect, the present invention provides an assay methodfor monitoring treatment with a tyrosine kinase inhibitor in a subject,the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from the subject; and    -   (b) monitoring the likelihood that the subject will develop        resistance to treatment with the tyrosine kinase inhibitor using        an algorithm based upon the at least one profile.

In a further aspect, the present invention provides an assay method foroptimizing dose efficacy in a subject receiving a tyrosine kinaseinhibitor, the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from the subject; and    -   (b) recommending a subsequent dose of the tyrosine kinase        inhibitor using an algorithm based upon the at least one        profile.

Other objects, features, and advantages of the present invention will beapparent to one of skill in the art from the following detaileddescription and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart of one embodiment of the present inventiondescribing an algorithm for individualizing gefitinib (Iressa®) therapyin patients with cancer.

FIG. 2 shows a flowchart of another embodiment of the present inventiondescribing an algorithm for individualizing sunitinib (Sutent®) therapyin patients with cancer.

FIG. 3 shows a flow diagram illustrating the various analyses that canbe performed on each whole blood fraction.

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present invention is based, in part, on the surprising discoverythat a combination of biomarkers can be used in an algorithmic approachto individualize tyrosine kinase inhibitor therapy in patients withcancers comprising solid tumors such as colorectal cancer, lung cancer,etc. Given the high inter-patient variability in response to tyrosinekinase inhibitors, the assay methods of the present invention areparticularly advantageous because they utilize a combinatorial strategythat takes into account differences in nucleic acid and/or proteinprofiles of multiple molecular determinants (i.e., biomarkers) todetermine whether a tumor or tumor cell from a patient has a highlikelihood of responding to treatment with a specific tyrosine kinaseinhibitor or combination of tyrosine kinase inhibitors. If the patientis classified as a responder, a dosing regimen tailored to that patientcan then be created to achieve therapeutic efficacy without inducingtoxic side-effects. Consequently, patients classified as responders canreceive the full benefits of tyrosine kinase inhibitor therapy withoutexperiencing the side-effects associated with such therapy. Similarly,patients already undergoing treatment with a tyrosine kinase inhibitorcan experience a reduction in toxic side-effects without compromisingtherapeutic efficacy by adjusting the subsequent dose of the drug.Likewise, patients already undergoing treatment with a tyrosine kinaseinhibitor can be monitored to assess whether resistance to the drug hasdeveloped and an alternative cancer therapy should be administered. As aresult, the methods of the present invention enable tyrosine kinaseinhibitors such as gefitinib and sunitinib to become first-linetherapeutic agents, rather than their current role as second- orthird-line cancer therapies. The tyrosine kinase inhibitors describedherein can be administered alone or co-administered (e.g., concurrentlyor sequentially) with conventional chemotherapy, radiation therapy,hormonal therapy, and/or immunotherapy for the treatment of cancer.

Currently, tumor tissue is analyzed using various individual biomarkersto give an indication of the appropriate therapy in patients with solidtumors. For example, HER2 or EGFR immunohistochemistry on tumor tissueis performed prior to prescribing trastuzumab (Herceptin®) or cetuximab(Erbitux®), respectively. However, there are substantial limitationsassociated with the use of tumor tissue for biomarker analysis. Inparticular, tumor tissue is only available pre-surgery or in patientswithout surgical therapy, formalin-fixation paraffin-embedding of tumortissue interferes with the analysis of many biomarkers, variability infixation processing alters the level of many biomarkers in tumor tissue,and for small tumors, as are increasingly detected in breast cancer,very little tumor tissue sample is left for biomarker analysis afterstandard pathology. In addition, tumor tissue is not available duringthe course of tyrosine kinase inhibitor therapy, so it cannot be usedfor monitoring efficacy or determining when a change in therapy isneeded. The present invention overcomes these limitations by utilizingfractional components obtained from a single sample. As a non-limitingexample, a whole blood sample which is separated into its liquid (e.g.,plasma, serum, etc.) and cellular (e.g., red blood cells, white bloodcells, platelets, etc.) components can be analyzed for an entirespectrum of biomarkers, thereby providing an advantageous means ofindividualizing tyrosine kinase inhibitor therapy according to themethods of the present invention.

As such, the present invention provides more accurate methods forpredicting, identifying, or monitoring the response of a tumor (e.g.,lung carcinoma, colorectal carcinoma, gastrointestinal stromal tumor,renal cell carcinoma, etc.), a tumor cell (e.g., a circulating tumorcell or circulating endothelial cell derived from a tumor), or a patientwho has been diagnosed with cancer to treatment with a specific tyrosinekinase inhibitor (e.g., gefitinib, sutent, etc.) or cocktail of tyrosinekinase inhibitors. The present invention is also useful for monitoringthe development of acquired resistance to treatment with one or moretyrosine kinase inhibitors in a patient who has been receiving the drug.In addition, the present invention finds utility in methods ofoptimizing tyrosine kinase inhibitor dosages (e.g., optimizing doseamount, optimizing dose efficacy, reducing drug toxicity, etc.) inpatients undergoing tyrosine kinase inhibitor therapy.

II. Definitions

As used herein, the following terms have the meanings ascribed to themunless specified otherwise.

The term “cancer” is intended to include any member of a class ofdiseases characterized by the uncontrolled growth of aberrant cells. Theterm includes all known cancers and neoplastic conditions, whethercharacterized as malignant, benign, soft tissue, or solid, and cancersof all stages and grades including pre- and post-metastatic cancers.Examples of different types of cancer include, but are not limited to,lung cancer (e.g., non-small cell lung cancer); digestive andgastrointestinal cancers such as colorectal cancer, gastrointestinalstromal tumors, gastrointestinal carcinoid tumors, colon cancer, rectalcancer, anal cancer, bile duct cancer, small intestine cancer, andstomach (gastric) cancer; esophageal cancer; gallbladder cancer; livercancer; pancreatic cancer; appendix cancer; breast cancer; ovariancancer; renal cancer (e.g., renal cell carcinoma); cancer of the centralnervous system; skin cancer; lymphomas; choriocarcinomas; head and neckcancers; osteogenic sarcomas; and blood cancers. As used herein, a“tumor” comprises one or more cancerous cells.

The term “tyrosine kinase” as used herein includes enzymes that catalyzethe transfer of the terminal phosphate of adenosine triphosphate (ATP)to tyrosine residues in protein substrates. Non-limiting examples oftyrosine kinases include receptor tyrosine kinases such as EGFR (e.g.,EGFR/HER1/ErbB1, HER2/Neu/ErbB2, HER3/ErbB3, HER4/ErbB4), INSR (insulinreceptor), IGF-IR, IGF-II1R, IRR (insulin receptor-related receptor),PDGFR (e.g., PDGFRA, PDGFRB), c-KIT/SCFR, VEGFR-1/FLT-1,VEGFR-2/FLK-1/KDR, VEGFR-3/FLT-4, FLT-3/FLK-2, CSF-1R, FGFR 1-4, CCK4,TRK A-C, MET, RON, EPHA 1-8, EPHB 1-6, AXL, MER, TYRO3, TIE, TEK, RYK,DDR 1-2, RET, c-ROS, LTK (leukocyte tyrosine kinase), ALK (anaplasticlymphoma kinase), ROR 1-2, MUSK, AATYK 1-3, and RTK 106; andnon-receptor tyrosine kinases such as BCR-ABL, Src, Frk, Btk, Csk, Abl,Zap70, Fes/Fps, Fak, Jak, Ack, and LIMK. One of skill in the art willknow of other receptor and/or non-receptor tyrosine kinases that can betargeted using the inhibitors described herein.

The term “tyrosine kinase inhibitor” includes any of a variety oftherapeutic agents or drugs that act as selective or non-selectiveinhibitors of receptor and/or non-receptor tyrosine kinases. Withoutbeing bound to any particular theory, tyrosine kinase inhibitorsgenerally inhibit target tyrosine kinases by binding to the ATP-bindingsite of the enzyme. Examples of tyrosine kinase inhibitors suitable foruse in the methods of the present invention include, but are not limitedto, gefitinib (Iressa®), sunitinib (Sutent®; SU11248), erlotinib(Tarceva®; OSI-1774), lapatinib (GW572016; GW2016), canertinib (CI1033), semaxinib (SU5416), vatalanib (PTK787/ZK222584), sorafenib (BAY43-9006), imatinib (Gleevec®; STI571), dasatinib (BMS-354825),leflunomide (SU101), vandetanib (Zactima™; ZD6474), derivatives thereof,analogs thereof, and combinations thereof. Additional tyrosine kinaseinhibitors suitable for use in the present invention are described in,e.g., U.S. Pat. Nos. 5,618,829, 5,639,757, 5,728,868, 5,804,396,6,100,254, 6,127,374, 6,245,759, 6,306,874, 6,313,138, 6,316,444,6,329,380, 6,344,459, 6,420,382, 6,479,512, 6,498,165, 6,544,988,6,562,818, 6,586,423, 6,586,424, 6,740,665, 6,794,393, 6,875,767,6,927,293, and 6,958,340. One of skill in the art will know of othertyrosine kinase inhibitors suitable for use in the present invention. Incertain instances, the tyrosine kinase inhibitor is administered in apharmaceutically acceptable form including, without limitation, analkali or alkaline earth metal salt such as an aluminum, calcium,lithium, magnesium, potassium, sodium, or zinc salt; an ammonium saltsuch as a tertiary amine or quaternary ammonium salt; and an acid saltsuch as a succinate, tartarate, bitartarate, dihydrochloride,salicylate, hemisuccinate, citrate, isocitrate, malate, maleate,mesylate, hydrochloride, hydrobromide, phosphate, acetate, carbamate,sulfate, nitrate, formate, lactate, gluconate, glucuronate, pyruvate,oxalacetate, fumarate, propionate, aspartate, glutamate, or benzoatesalt.

As used herein, the term “biomarker” or “marker” includes anybiochemical marker, serological marker, genetic marker, or otherclinical or echographic characteristic that can be used in predicting,identifying, evaluating, assessing, determining, monitoring, and/oroptimizing tyrosine kinase inhibitor efficacy, toxicity, and/orresistance according to the methods of the present invention. Examplesof biochemical or serological markers include, without limitation,tyrosine kinases such as the receptor and non-receptor tyrosine kinasesdescribed above; growth factors (e.g., TGF-α, EGF, VEGF, PDGF,amphiregulin, HB-EGF (heparin-binding EGF-like growth factor),betacellulin, heregulin, etc.); tumor suppressors (e.g., PTEN(phosphatase and tensin homolog deleted on chromosome 10), DMBT1(deleted in malignant brain tumors 1), LGI1 (leucine-rich gene-gliomainactivated 1), p53, etc.); and tyrosine kinase signaling components(e.g., Akt, MAPK/ERK, MEK, RAF, PLA2, MEKK, JNKK, JNK, p38, PI3K, Ras,Rho, PLC, PKC, p70 S6 kinase, p53, cyclin D1, STAT1, STAT3, PIP2, PIP3,PDK, mTOR, BAD, p21, p27, ROCK, IP3, TSP-1, NOS, etc.). Examples ofgenetic markers include, without limitation, tyrosine kinases such asthe receptor and non-receptor tyrosine kinases described above and smallGTPases such as Ras (e.g., K-Ras, N-Ras, H-Ras, etc.), Rho, Rac 1, andCdc42. In some embodiments, the genetic markers described herein aregenotyped to detect the presence or absence of a variant allele, e.g.,an activating mutation. Preferably, one or more biochemical orserological markers are measured in combination with one or more geneticmarkers. One skilled in the art will appreciate that biochemical orserological markers can also be categorized as genetic markers and viceversa.

As used herein, the term “profile” includes any set of data thatrepresents the distinctive features or characteristics associated with atumor, tumor cell, and/or cancer. The term encompasses a “nucleic acidprofile” that analyzes one or more genetic markers, a “protein profile”that analyzes one or more biochemical or serological markers, andcombinations thereof. Examples of nucleic acid profiles include, but arenot limited to, a genotypic profile, gene copy number profile, geneexpression profile, DNA methylation profile, and combinations thereof.Non-limiting examples of protein profiles include a protein expressionprofile, protein activation profile, and combinations thereof. Forexample, a “genotypic profile” includes a set of genotypic data thatrepresents the genotype of one or more genes associated with a tumor,tumor cell, and/or cancer. Similarly, a “gene copy number profile”includes a set of gene copy number data that represents theamplification of one or more genes associated with a tumor, tumor cell,and/or cancer. Likewise, a “gene expression profile” includes a set ofgene expression data that represents the mRNA levels of one or moregenes associated with a tumor, tumor cell, and/or cancer. In addition, a“DNA methylation profile” includes a set of methylation data thatrepresents the DNA methylation levels (e.g., methylation status) of oneor more genes associated with a tumor, tumor cell, and/or cancer.Furthermore, a “protein expression profile” includes a set of proteinexpression data that represents the levels of one or more proteinsassociated with a tumor, tumor cell, and/or cancer. Moreover, a “proteinactivation profile” includes a set of data that represents theactivation (e.g., phosphorylation status) of one or more proteinsassociated with a tumor, tumor cell, and/or cancer.

The term “gene” includes the segment of DNA involved in producing apolypeptide chain. Specifically, a gene includes, without limitation,regions preceding and following the coding region, such as the promoterand 3′-untranslated region, respectively, as well as interveningsequences (introns) between individual coding segments (exons).

The term “nucleic acid” or “polynucleotide” includesdeoxyribonucleotides or ribonucleotides and polymers thereof in eithersingle- or double-stranded form. Unless specifically limited, the termencompasses nucleic acids containing known analogues of naturalnucleotides that have similar binding properties as the referencenucleic acid and are metabolized in a manner similar to naturallyoccurring nucleotides. Unless otherwise indicated, a particular nucleicacid sequence also implicitly encompasses conservatively modifiedvariants thereof (e.g., degenerate codon substitutions), alleles,orthologs, SNPs, and complementary sequences as well as the sequenceexplicitly indicated. Specifically, degenerate codon substitutions maybe achieved by generating sequences in which the third position of oneor more selected (or all) codons is substituted with mixed-base and/ordeoxyinosine residues (Batzer et al., Nucleic Acid Res., 19:5081 (1991);Ohtsuka et al., J. Biol. Chem., 260:2605-2608 (1985); Rossolini et al.,Mol. Cell. Probes, 8:91-98 (1994)). The term nucleic acid is usedinterchangeably with gene, cDNA, and mRNA encoded by a gene.

The term “polymorphism” include the occurrence of two or moregenetically determined alternative sequences or alleles in a population.A “polymorphic site” includes the locus at which divergence occurs.Preferred polymorphic sites have at least two alleles, each occurring atfrequency of greater than 1%, and more preferably greater than 10% or20% of a selected population. A polymorphic locus can be as small as onebase pair (single nucleotide polymorphism, or SNP) or can comprise aninsertion or deletion of multiple nucleotides. Polymorphic markersinclude, but are not limited to, restriction fragment lengthpolymorphisms, variable number of tandem repeats (VNTR's), hypervariableregions, minisatellites, dinucleotide repeats, trinucleotide repeats,tetranucleotide repeats, simple sequence repeats, and insertion elementssuch as Alu. The first identified allele is arbitrarily designated asthe reference allele and other alleles are designated as alternative or“variant alleles.” The allele occurring most frequently in a selectedpopulation is sometimes referred to as the “wild-type” allele. Diploidorganisms may be homozygous or heterozygous for the variant alleles. Thevariant allele may or may not produce an observable physical orbiochemical characteristic (“phenotype”) in an individual carrying thevariant allele. For example, a variant allele may alter the enzymaticactivity of a protein encoded by a gene of interest.

A “single nucleotide polymorphism” or “SNP” occurs at a polymorphic siteoccupied by a single nucleotide, which is the site of variation betweenallelic sequences. The site is usually preceded by and followed byhighly conserved sequences of the allele (e.g., sequences that vary inless than 1/100 or 1/1000 members of the populations). A SNP usuallyarises due to substitution of one nucleotide for another at thepolymorphic site. A transition is the replacement of one purine byanother purine or one pyrimidine by another pyrimidine. A transversionis the replacement of a purine by a pyrimidine or vice versa. Singlenucleotide polymorphisms can also arise from a deletion of a nucleotideor an insertion of a nucleotide relative to a reference allele.

The term “genotype” as used herein includes to the genetic compositionof an organism, including, for example, whether a diploid organism isheterozygous or homozygous for one or more variant alleles of interest.

The term “gene amplification” comprises a cellular process characterizedby the production of multiple copies of any particular piece of DNA. Forexample, a tumor cell amplifies, or copies, chromosomal segmentsnaturally as a result of cell signals and sometimes environmentalevents. The process of gene amplification leads to the production ofmany copies of the genes that are located on that region of thechromosome. In certain instances, so many copies of the amplified regionare produced that they can form their own small pseudo-chromosomescalled double-minute chromosomes. The genes on each of the copies can betranscribed and translated, leading to an overproduction of the mRNA andprotein corresponding to the amplified genes.

The term “subject” or “patient” typically includes humans, but can alsoinclude other animals such as, e.g., other primates, rodents, canines,felines, equines, ovines, porcines, and the like.

The term “sample” as used herein includes any biological specimenobtained from a subject. Samples include, without limitation, wholeblood, plasma, serum, red blood cells, white blood cells (e.g.,peripheral blood mononuclear cells), saliva, urine, stool (i.e., feces),tears, nipple aspirate, lymph, fine needle aspirate, any other bodilyfluid, a tissue sample (e.g., tumor tissue) such as a biopsy of a tumor,and cellular extracts thereof. In some embodiments, the sample is wholeblood or a fractional component thereof such as plasma, serum, or a cellpellet. In preferred embodiments, the sample is obtained by isolatingcirculating cells of a solid tumor from a whole blood cell pellet usingany technique known in the art. As used herein, the term “circulatingcells” comprises cells that have either metastasized ormicrometastasized from a solid tumor and includes circulating tumorcells, cancer stem cells, and/or cells that are migrating to the tumor(e.g., circulating endothelial progenitor cells, circulating endothelialcells, circulating pro-angiogenic myeloid cells, circulating dendriticcells, etc.). In other embodiments, the sample is a formalin fixedparaffin embedded (FFPE) tumor tissue sample, e.g., from a solid tumorof the lung, colon, or rectum.

The term “course of therapy” or “therapy” includes any therapeuticapproach taken to relieve and/or prevent one or more symptoms associatedwith cancer. The term encompasses administering any compound, drug,therapeutic agent, procedure, or regimen useful for improving the healthof a subject with cancer. One skilled in the art will appreciate thateither the course of therapy or the dose of the current course oftherapy can be changed based upon the panel of biomarkers determinedusing the methods of the present invention. Examples of therapiessuitable for use in the methods of the present invention include,without limitation, targeted cancer therapy using tyrosine kinaseinhibitors, conventional chemotherapy, radiation therapy, hormonaltherapy, immunotherapy, and combinations thereof.

The term “recommending” as used herein includes providing dosinginstructions for a tyrosine kinase inhibitor or alternative cancertherapy based on the nucleic acid and/or protein profiles determined fora particular subject. In some embodiments, the methods of the presentinvention provide a recommendation of an initial dose of the drug. Inother embodiments, the methods of the present invention provide arecommendation of a subsequent dose of the drug or an alternativetherapy. Dosing instructions include, without limitation, lab resultswith preferred drug doses, data sheets, look-up tables setting forthpreferred drug doses, instructions or guidelines for using the drug,package inserts to accompany the drug, and the like. In certainembodiments, the term “recommending” associates the result obtained fromthe use of a particular algorithm (e.g., index value) with side-effectsor efficacy.

As used herein, the term “administering” includes oral administration,administration as a suppository, topical contact, intravenous,intraperitoneal, intramuscular, intralesional, intrathecal, intranasalor subcutaneous administration, or the implantation of a slow-releasedevice, e.g., a mini-osmotic pump, to a subject. Administration is byany route, including parenteral and transmucosal (e.g., buccal,sublingual, palatal, gingival, nasal, vaginal, rectal, or transdermal).Parenteral administration includes, e.g., intravenous, intramuscular,intra-arteriole, intradermal, subcutaneous, intraperitoneal,intraventricular, and intracranial. Other modes of delivery include, butare not limited to, the use of liposomal formulations, intravenousinfusion, transdermal patches, etc. By “co-administer” it is meant thata tyrosine kinase inhibitor is administered at the same time, just priorto, or just after the administration of one or more additional drugs ortherapeutic regimens (e.g., other tyrosine kinase inhibitors; otheranti-cancer agents such as chemotherapeutic agents, monoclonalantibodies, antibiotics, immunosuppressive agents, and anti-inflammatoryagents; other cancer therapies such as radiation therapy, hormonaltherapy, and immunotherapy; etc.).

The term “identifying the response of a tumor to treatment with atyrosine kinase inhibitor” includes the use of the algorithms of thepresent invention to determine whether a tumor or tumor cell isresponsive (i.e., sensitive) or non-responsive (i.e., resistant) to theeffects of a particular tyrosine kinase inhibitor or combinationsthereof. Generally, a tumor or tumor cell which is responsive totreatment with a tyrosine kinase inhibitor exhibits an improvement inone or more desired results (e.g., tumor cell death, inhibition of tumorgrowth, reduction in tumor size, prevention of tumor metastasis, etc.)when compared to the absence of treatment in control samples. In certaininstances, a tumor or tumor cell is considered to be responsive totreatment with a tyrosine kinase inhibitor when it responds to initialtreatment but then develops resistance as treatment is continued.

The term “predicting the response of a subject to treatment with atyrosine kinase inhibitor” includes the use of the algorithms of thepresent invention to determine whether a subject would likely respond toa particular tyrosine kinase inhibitor or combinations thereof. Althoughcancer is used herein as a non-limiting example, one skilled in the artwill appreciate that subjects having other diseases or disorders inwhich tyrosine kinase inhibitors provide some therapeutic benefit canalso be evaluated according to the methods of the present invention.Generally, a patient who is responsive to treatment with a tyrosinekinase inhibitor exhibits an improvement in one or more desired clinicalresults (e.g., alleviation of symptoms, diminishment of the extent ofcancer, stabilization of cancer, delaying or slowing the progression ofcancer, amelioration of cancer, remission, etc.) when compared to theabsence of treatment (e.g., placebo) in control patients. In certaininstances, a patient is considered to be responsive to treatment with atyrosine kinase inhibitor when that patient responds to initialtreatment but then develops resistance as treatment is continued.

The term “monitoring treatment with a tyrosine kinase inhibitor in asubject” includes the use of the algorithms of the present invention todetermine whether a subject will develop or has developed resistance totreatment with a tyrosine kinase inhibitor. In certain instances, theresult obtained from the use of a particular algorithm indicates thatthe subject has an increased likelihood of developing or has developedresistance to tyrosine kinase inhibitor therapy. In certain otherinstances, the result obtained from the use of a particular algorithmindicates that the subject has a decreased likelihood of developing orhas not developed resistance to tyrosine kinase inhibitor therapy.

The term “optimizing dose efficacy in a subject receiving a tyrosinekinase inhibitor” includes the use of the algorithms of the presentinvention to adjust the subsequent dose of the tyrosine kinase inhibitoror to change the course of therapy for a subject after the drug has beenadministered in order to optimize its therapeutic efficacy. In certaininstances, the result obtained from the use of a particular algorithmindicates that the subsequent dose of the tyrosine kinase inhibitorshould be increased, decreased, or maintained. In certain otherinstances, the result obtained from the use of a particular algorithmindicates that an alternative cancer therapy should be administered tothe subject.

III. Description of the Embodiments

The present invention provides methods for analyzing a combination ofbiomarkers in a sample such as whole blood to individualize tyrosinekinase inhibitor therapy in subjects who have been diagnosed withcancer. As a result, the present invention enables tyrosine kinaseinhibitors such as gefitinib and sunitinib to become first-linetherapeutic agents for the treatment of solid tumors, rather than theircurrent role as second- or third-line cancer therapies.

Accordingly, in one aspect, the present invention provides an assaymethod for identifying the response of a tumor to treatment with atyrosine kinase inhibitor, the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from a subject; and    -   (b) identifying the tumor as responsive or non-responsive to        treatment with the tyrosine kinase inhibitor using an algorithm        based upon the at least one profile.

In one embodiment, the tumor comprises a solid tumor of a tissueselected from the group consisting of lung, colon, rectum, gall bladder,brain, breast, kidney, pancreas, stomach, liver, bone, skin, spleen,ovary, testis, prostate, and muscle. Preferably, the tumor is non-smallcell lung carcinoma, a gastrointestinal stromal tumor, colorectalcarcinoma, or renal cell carcinoma. In another embodiment, the subjecthas been diagnosed with cancer.

In some embodiments, the sample comprises a whole blood, serum, plasma,urine, nipple aspirate, lymph, saliva, fine needle aspirate, and/ortumor tissue sample. In certain instances, the whole blood sample isseparated into a plasma or serum fraction and a cellular fraction (i.e.,cell pellet). The cellular fraction typically contains red blood cells,white blood cells, and/or circulating cells of a solid tumor such ascirculating tumor cells (CTCs) and circulating endothelial cells (CECs).The plasma or serum fraction usually contains, inter alia, nucleic acids(e.g., DNA, RNA) and proteins that are released by CTCs and/or CECs. Thecirculating cells can be isolated using one or more separation methodsincluding, for example, immunomagnetic separation (see, e.g., Racila etal., Proc. Natl. Acad. Sci. USA, 95:4589-4594 (1998); Bilkenroth et al.,Int. J. Cancer, 92:577-582 (2001)), microfluidic separation (see, e.g.,Mohamed et al., IEEE Trans. Nanobiosci., 3:251-256 (2004)), FACS (see,e.g., Mancuso et al., Blood, 97:3658-3661 (2001)), density gradientcentrifugation (see, e.g., Baker et al., Clin. Cancer Res., 13:4865-4871(2003)), and depletion methods (see, e.g., Meye et al., Int. J. Oncol.,21:521-530 (2002)). In some instances, the isolated circulating cellscan be stimulated in vitro with one or more growth factors before,during, and/or after incubation with one or more tyrosine kinaseinhibitors of interest. In other instances, the isolated circulatingcells can be lysed, e.g., following growth factor stimulation, toproduce a cellular extract (e.g., tumor cell lysate) using any techniqueknown in the art.

In other embodiments, the tyrosine kinase inhibitor comprises anepidermal growth factor receptor (EGFR) inhibitor, vascular endothelialcell growth factor receptor (VEGFR) inhibitor, platelet-derived growthfactor receptor (PDGFR) inhibitor, c-KIT inhibitor, FMS-like tyrosinekinase 3 (FLT-3) inhibitor, BCR-ABL inhibitor, and combinations thereof.Examples of EGFR inhibitors include, but are not limited to, gefitinib,erlotinib, lapatinib, canertinib, sorafenib, vandetanib,pharmaceutically acceptable salts thereof, and combinations thereof.Non-limiting examples of VEGFR inhibitors include sunitinib, semaxinib,vatalanib, sorafenib, vandetanib, pharmaceutically acceptable saltsthereof, and combinations thereof. Examples of PDGFR inhibitors include,without limitation, sunitinib, imatinib, sorafenib, leflunomide,pharmaceutically acceptable salts thereof, and combinations thereof.Non-limiting examples of c-KIT inhibitors include sunitinib, imatinib,semaxinib, pharmaceutically acceptable salts thereof, and combinationsthereof. Examples of FLT-3 inhibitors include, but are not limited to,sunitinib, semaxinib, pharmaceutically acceptable salts thereof, andcombinations thereof. Examples of BCR-ABL inhibitors include, withoutlimitation, imatinib and a pharmaceutically acceptable salt thereof.

In another embodiment, the nucleic acid profile comprises a genotypicprofile, gene copy number profile, gene expression profile, DNAmethylation profile, and combinations thereof.

In certain instances, the genotypic profile comprises determining thegenotype of at least one gene selected from the group consisting of atyrosine kinase gene, small GTPase gene, and combinations thereof. Thegenotype can be determined at a polymorphic site such as a singlenucleotide polymorphism (SNP). In a preferred embodiment, the tyrosinekinase gene is selected from the group consisting of an EGFR gene, VEGFRgene, PDGFR gene, c-KIT gene, FLT-3 gene, BCR-ABL gene, and combinationsthereof. Examples of EGFR genes include, but are not limited to, an EGFR(HER1/ErbB1) gene, HER2 (Neu/ErbB2) gene, HER3 (ErbB3) gene, HER4(ErbB4) gene, and combinations thereof. Non-limiting examples of smallGTPase genes include a Ras gene, Rho gene, Rac1 gene, Cdc42 gene, andcombinations thereof. Preferably, the Ras gene is selected from thegroup consisting of a K-Ras gene, N-Ras gene, H-Ras gene, andcombinations thereof.

In certain instances, the gene copy number profile comprises determiningthe number of copies of at least one tyrosine kinase gene. In apreferred embodiment, the at least one tyrosine kinase gene is selectedfrom the group consisting of an EGFR gene, VEGFR gene, PDGFR gene, c-KITgene, FLT-3 gene, BCR-ABL gene, and combinations thereof. As anon-limiting example, gene amplification can be detected in one or moremembers of the EGFR family of genes, e.g., EGFR (HER1/ErbB1), HER2(Neu/ErbB2), HER3 (ErbB3), and/or HER4 (ErbB4). Preferably, the numberof copies of said at least one tyrosine kinase gene is determined byfluorescence in situ hybridization (FISH). Alternatively, geneamplification can be measured using chromogenic in situ hybridization(CISH) or immunohistochemistry (IHC).

In certain instances, the gene expression profile comprises determiningthe expression level of at least one tyrosine kinase gene. Theexpression level of any of the tyrosine kinase genes described hereincan be analyzed. Preferably, the expression level of the at least onetyrosine kinase gene is determined by measuring mRNA levels.

In certain instances, the DNA methylation profile comprises determiningthe methylation state of at least one tumor suppressor gene. As anon-limiting example, DNA methylation can be detected in tumorsuppressor genes such as PTEN, DMBT1, LGI1, p53, CDKN2B, ESR1 (humanestrogen receptor 1), ICSBP (interferon consensus-binding protein), ETV3(Ets variant 3), DDX20 (DEAD box polypeptide), and combinations thereof.The level of DNA methylation can be measured using any method known toone of skill in the art, such as those techniques described below.

In a further embodiment, the protein profile comprises a proteinexpression profile, protein activation profile, and combinationsthereof.

In certain instances, the protein expression profile comprisesdetermining the expression level of at least one protein selected fromthe group consisting of a tyrosine kinase, growth factor, tumorsuppressor, and combinations thereof. In a preferred embodiment, thetyrosine kinase is selected from the group consisting of EGFR, VEGFR,PDGFR, c-KIT, FLT-3, BCR-ABL, and combinations thereof. As anon-limiting example, an expression level can be measured for one ormore members of the EGFR family, e.g., EGFR (HER1/ErbB1), HER2(Neu/ErbB2), HER3 (ErbB3), and/or HER4 (ErbB4). Examples of growthfactors include, but are not limited to, TGF-α, EGF, VEGF, PDGF, andcombinations thereof. Non-limiting examples of tumor suppressors includePTEN, DMBT1, LGI1, and combinations thereof. Preferably, the expressionlevel of the at least one protein is determined by IHC or an immunoassaysuch as an enzyme-linked immunosorbent assay (ELISA).

In certain instances, the protein activation profile comprisesdetermining the phosphorylation state, ubiquitination state, and/orcomplexation state of at least one protein selected from the groupconsisting of a tyrosine kinase, tyrosine kinase signaling component,and combinations thereof. In one embodiment, the tyrosine kinase isselected from the group consisting of EGFR, VEGFR, PDGFR, c-KIT, FLT-3,BCR-ABL, and combinations thereof. As a non-limiting example, thecomplexation state of members of the EGFR family, e.g., EGFR(HER1/ErbB1), HER2 (Neu/ErbB2), HER3 (ErbB3), and/or HER4 (ErbB4), canbe determined by detecting the presence or level of one or more EGFRheterodimeric complexes (e.g., ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4,etc.). In another embodiment, the at least one tyrosine kinase signalingcomponent is selected from the group consisting of Akt, MAPK/ERK, MEK,RAF, PLA2, MEKK, JNKK, JNK, p38, PI3K, Ras, Rho, PLC, PKC, p70 S6kinase, p53, cyclin D1, STAT1, STAT3, PIP2, PIP3, PDK, mTOR, BAD, p21,p27, ROCK, IP3, TSP-1, NOS, and combinations thereof. In some instances,the phosphorylation state of the tyrosine kinase and/or tyrosine kinasesignaling component can be determined by IHC. Other techniques includeperforming an immunoassay such as an ELISA to assess the phosphorylationstate of one or more proteins of interest.

In another embodiment, the algorithm is used to calculate an indexvalue. In certain instances, the index value comprises a cumulativeindex value. Typically, the cumulative index value is compared to anindex cutoff value. In certain instances, a cumulative index value thatis greater than or equal to the index cutoff value indicates that thesubject is responsive or has an increased likelihood of responding totreatment with the tyrosine kinase inhibitor. In these instances, themethod can further comprise recommending a dose (e.g., a therapeuticallyeffective dose) of the tyrosine kinase inhibitor to be administered tothe subject. In certain other instances, a cumulative index value thatis greater than or equal to the index cutoff value indicates that thesubject is non-responsive or has a decreased likelihood of responding totreatment with the tyrosine kinase inhibitor. In these instances, themethod can further comprise recommending a dose of another tyrosinekinase inhibitor or an alternative cancer therapy to be administered tothe subject.

In some embodiments, identifying a tumor as responsive or non-responsiveto treatment with a tyrosine kinase inhibitor is based upon determiningat least one nucleic acid and/or protein profile in conjunction with theuse of a learning statistical classifier system. The learningstatistical classifier system can be selected from the group consistingof a random forest (RF), classification and regression tree (C&RT),boosted tree, neural network (NN), support vector machine (SVM), generalchi-squared automatic interaction detector model, interactive tree,multiadaptive regression spline, machine learning classifier, andcombinations thereof. Preferably, the learning statistical classifiersystem is a tree-based statistical algorithm (e.g., RF, C&RT, etc.)and/or a neural network (e.g., artificial NN (ANN), etc.).

In certain instances, the algorithm comprises a single learningstatistical classifier system. Preferably, the single learningstatistical classifier system comprises a tree-based statisticalalgorithm such as a RF or C&RT or a neural network such as an ANN. As anon-limiting example, a single learning statistical classifier systemcan be used to identify the tumor as responsive or non-responsive totreatment based upon a prediction or probability value and the at leastone nucleic acid and/or protein profile. The use of a single learningstatistical classifier system typically identifies the tumor assensitive or resistant to the tyrosine kinase inhibitor of interest witha sensitivity, specificity, positive predictive value, negativepredictive value, and/or overall accuracy of at least about 60%, 65%,70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In certain other instances, the algorithm comprises a combination of atleast two learning statistical classifier systems. Preferably, thecombination of learning statistical classifier systems comprises a RFand a NN, e.g., used in tandem or parallel. As a non-limiting example, aRF can first be used to generate a prediction or probability value basedupon the at least one-nucleic acid and/or protein profile, and a NN canthen be used to identify the tumor as responsive or non-responsive totreatment with a tyrosine kinase inhibitor based upon the prediction orprobability value and the at least one nucleic acid and/or proteinprofile. Advantageously, the hybrid RF/NN learning statisticalclassifier system of the present invention identifies the tumor assensitive or resistant to the tyrosine kinase inhibitor of interest witha sensitivity, specificity, positive predictive value, negativepredictive value, and/or overall accuracy of at least about 60%, 65%,70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

In certain embodiments, the methods of the present invention furthercomprise sending the identification results (i.e., whether the tumor isresponsive or non-responsive to treatment with the tyrosine kinaseinhibitor) to a clinician, e.g., an oncologist or a generalpractitioner.

In another aspect, the present invention provides an assay method forpredicting the response of a subject to treatment with a tyrosine kinaseinhibitor, the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from the subject; and    -   (b) predicting the likelihood that the subject will respond to        treatment with the tyrosine kinase inhibitor using an algorithm        based upon the at least one profile.

In one embodiment, the subject has been diagnosed with cancer, e.g., asolid tumor of a tissue selected from the group consisting of lung,colon, rectum, gall bladder, brain, breast, kidney, pancreas, stomach,liver, bone, skin, spleen, ovary, testis, prostate, and muscle.Preferably, the cancer is non-small cell lung cancer, a gastrointestinalstromal tumor, colorectal cancer, or renal cell carcinoma.

In some embodiments, the sample comprises a whole blood, serum, plasma,urine, nipple aspirate, lymph, saliva, fine needle aspirate, and/ortumor tissue sample. In certain instances, the whole blood sample isseparated into a plasma or serum fraction and a cellular fraction (i.e.,cell pellet). The circulating cells of the solid tumor can be isolatedfrom the cellular fraction using one or more of the separation methodsdescribed above. In some instances, the isolated circulating cells canbe stimulated in vitro with one or more growth factors before, during,and/or after incubation with one or more tyrosine kinase inhibitors ofinterest. In other instances, the isolated circulating cells can belysed, e.g., following growth factor stimulation, to produce a cellularextract (e.g., tumor cell lysate) using any technique known in the art.

In other embodiments, the tyrosine kinase inhibitor is selected from thegroup consisting of an EGFR inhibitor, VEGFR inhibitor, PDGFR inhibitor,c-KIT inhibitor, FLT-3 inhibitor, BCR-ABL inhibitor, and combinationsthereof. Examples of inhibitors belonging to each class are describedabove.

In another embodiment, the nucleic acid profile is selected from thegroup consisting of a genotypic profile, gene copy number profile, geneexpression profile, DNA methylation profile, and combinations thereof.In a further embodiment, the protein profile is selected from the groupconsisting of a protein expression profile, protein activation profile,and combinations thereof. Non-limiting examples of techniques that canbe used to determine these nucleic acid and protein profiles aredescribed above.

In some embodiments, the algorithm is used to calculate an index value.In certain instances, the index value comprises a cumulative indexvalue. Typically, the cumulative index value is compared to an indexcutoff value. In certain instances, a cumulative index value that isgreater than or equal to the index cutoff value indicates that thesubject has an increased likelihood of responding to treatment with thetyrosine kinase inhibitor. In these instances, the method can furthercomprise recommending a dose (e.g., a therapeutically effective dose) ofthe tyrosine kinase inhibitor to be administered to the subject. Incertain other instances, a cumulative index value that is greater thanor equal to the index cutoff value indicates that the subject has adecreased likelihood of responding to treatment with the tyrosine kinaseinhibitor. In these instances, the method can further compriserecommending a dose of another tyrosine kinase inhibitor or analternative cancer therapy to be administered to the subject.

In some embodiments, predicting the likelihood that a subject willrespond to treatment with a tyrosine kinase inhibitor is based upondetermining at least one nucleic acid and/or protein profile inconjunction with the use of a learning statistical classifier system.The learning statistical classifier system can be selected from thegroup consisting of a random forest (RF), classification and regressiontree (C&RT), boosted tree, neural network (NN), support vector machine(SVM), general chi-squared automatic interaction detector model,interactive tree, multiadaptive regression spline, machine learningclassifier, and combinations thereof. Preferably, the learningstatistical classifier system is a tree-based statistical algorithm(e.g., RF, C&RT, etc.) and/or a neural network (e.g., artificial NN(ANN), etc.).

In certain instances, the algorithm comprises a single learningstatistical classifier system. As a non-limiting example, a singlelearning statistical classifier system can be used to predict thelikelihood that the subject will respond to treatment based upon aprediction or probability value and the at least one nucleic acid and/orprotein profile. The use of a single learning statistical classifiersystem typically predicts the likelihood that the subject will respondto the tyrosine kinase inhibitor of interest with a sensitivity,specificity, positive predictive value, negative predictive value,and/or overall accuracy of at least about 60%, 65%, 70%, 75%, 76%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In certain other instances, the algorithm comprises a combination of atleast two learning statistical classifier systems. Preferably, thecombination of learning statistical classifier systems comprises a RFand a NN, e.g., used in tandem or parallel. As a non-limiting example, aRF can first be used to generate a prediction or probability value basedupon the at least one nucleic acid and/or protein profile, and a NN canthen be used to predict the likelihood that the subject will respond totreatment with a tyrosine kinase inhibitor based upon the prediction orprobability value and the at least one nucleic acid and/or proteinprofile. Advantageously, the hybrid RF/NN learning statisticalclassifier system of the present invention predicts the likelihood thatthe subject will respond to the tyrosine kinase inhibitor of interestwith a sensitivity, specificity, positive predictive value, negativepredictive value, and/or overall accuracy of at least about 60%, 65%,70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

In certain embodiments, the methods of the present invention furthercomprise sending the prediction results (i.e., whether the subject islikely to respond to treatment with the tyrosine kinase inhibitor) to aclinician, e.g., an oncologist or a general practitioner.

In yet another aspect, the present invention provides an assay methodfor monitoring treatment with a tyrosine kinase inhibitor in a subject,the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from the subject; and    -   (b) monitoring the likelihood that the subject will develop        resistance to treatment with the tyrosine kinase inhibitor using        an algorithm based upon the at least one profile.

In one embodiment, the subject has been diagnosed with cancer, e.g., asolid tumor of a tissue selected from the group consisting of lung,colon, rectum, gall bladder, brain, breast, kidney, pancreas, stomach,liver, bone, skin, spleen, ovary, testis, prostate, and muscle.Preferably, the cancer is non-small cell lung cancer, a gastrointestinalstromal tumor, colorectal cancer, or renal cell carcinoma.

In some embodiments, the sample comprises a whole blood, serum, plasma,urine, nipple aspirate, lymph, saliva, fine needle aspirate, and/ortumor tissue sample. In certain instances, the whole blood sample isseparated into a plasma or serum fraction and a cellular fraction (i.e.,cell pellet). The circulating cells of the solid tumor can be isolatedfrom the cellular fraction using one or more of the separation methodsdescribed above. In some instances, the isolated circulating cells canbe stimulated in vitro with one or more growth factors before, during,and/or after incubation with one or more tyrosine kinase inhibitors ofinterest. In other instances, the isolated circulating cells can belysed, e.g., following growth factor stimulation, to produce a cellularextract (e.g., tumor cell lysate) using any technique known in the art.

In other embodiments, the tyrosine kinase inhibitor is selected from thegroup consisting of an EGFR inhibitor, VEGFR inhibitor, PDGFR inhibitor,c-KIT inhibitor, FLT-3 inhibitor, BCR-ABL inhibitor, and combinationsthereof. Examples of inhibitors belonging to each class are describedabove.

In another embodiment, the nucleic acid profile is selected from thegroup consisting of a genotypic profile, gene copy number profile, geneexpression profile, DNA methylation profile, and combinations thereof.In a further embodiment, the protein profile is selected from the groupconsisting of a protein expression profile, protein activation profile,and combinations thereof. Non-limiting examples of techniques that canbe used to determine these nucleic acid and protein profiles aredescribed above.

In some embodiments, the algorithm is used to calculate an index value.In certain instances, the index value comprises a cumulative indexvalue. Typically, the cumulative index value is compared to an indexcutoff value. In certain instances, a cumulative index value that isgreater than or equal to the index cutoff value indicates that thesubject has an increased likelihood of developing or has developedresistance to treatment with the tyrosine kinase inhibitor. In theseinstances, the method can further comprise recommending a dose ofanother tyrosine kinase inhibitor or an alternative therapy to beadministered to the subject. In certain other instances, a cumulativeindex value that is greater than or equal to the index cutoff valueindicates that the subject has a decreased likelihood of developing orhas not developed resistance to treatment with the tyrosine kinaseinhibitor. In these instances, the method can further compriserecommending that a subsequent dose of the tyrosine kinase inhibitor bemaintained.

In other embodiments, the method can further comprise comparing thecumulative index value to a cumulative index value generated at anearlier time. In certain instances, an increase in the cumulative indexvalue indicates that the subject has an increased likelihood ofdeveloping or has developed resistance to treatment with the tyrosinekinase inhibitor. In certain other instances, an increase in thecumulative index value indicates that the subject has a decreasedlikelihood of developing or has not developed resistance to treatmentwith the tyrosine kinase inhibitor.

In some embodiments, monitoring the likelihood that a subject willdevelop resistance to treatment with a tyrosine kinase inhibitor isbased upon determining at least one nucleic acid and/or protein profilein conjunction with the use of a learning statistical classifier system.The learning statistical classifier system can be selected from thegroup consisting of a random forest (RF), classification and regressiontree (C&RT), boosted tree, neural network (NN), support vector machine(SVM), general chi-squared automatic interaction detector model,interactive tree, multiadaptive regression spline, machine learningclassifier, and combinations thereof. Preferably, the learningstatistical classifier system is a tree-based statistical algorithm(e.g., RF, C&RT, etc.) and/or a neural network (e.g., artificial NN(ANN), etc.).

In certain instances, the algorithm comprises a single learningstatistical classifier system. As a non-limiting example, a singlelearning statistical classifier system can be used to monitor thelikelihood that the subject will develop resistance to treatment basedupon a prediction or probability value and the at least one nucleic acidand/or protein profile. The use of a single learning statisticalclassifier system typically monitors the likelihood that the subjectwill develop resistance to the tyrosine kinase inhibitor of interestwith a sensitivity, specificity, positive predictive value, negativepredictive value, and/or overall accuracy of at least about 60%, 65%,70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In certain other instances, the algorithm comprises a combination of atleast two learning statistical classifier systems. Preferably, thecombination of learning statistical classifier systems comprises a RFand a NN, e.g., used in tandem or parallel. As a non-limiting example, aRF can first be used to generate a prediction or probability value basedupon the at least one nucleic acid and/or protein profile, and a NN canthen be used to monitor the likelihood that the subject will developresistance to treatment with a tyrosine kinase inhibitor based upon theprediction or probability value and the at least one nucleic acid and/orprotein profile. Advantageously, the hybrid RF/NN learning statisticalclassifier system of the present invention monitors the likelihood thatthe subject will develop resistance to the tyrosine kinase inhibitor ofinterest with a sensitivity, specificity, positive predictive value,negative predictive value, and/or overall accuracy of at least about60%, 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

In certain embodiments, the methods of the present invention furthercomprise sending the monitoring results (i.e., whether the subject islikely to develop resistance to treatment with the tyrosine kinaseinhibitor) to a clinician, e.g., an oncologist or a generalpractitioner.

In a further aspect, the present invention provides an assay method foroptimizing dose efficacy in a subject receiving a tyrosine kinaseinhibitor, the method comprising:

-   -   (a) determining at least one profile selected from the group        consisting of a nucleic acid profile, protein profile, and        combinations thereof in a sample from the subject; and    -   (b) recommending a subsequent dose of the tyrosine kinase        inhibitor using an algorithm based upon the at least one        profile.

In one embodiment, the subject has been diagnosed with cancer, e.g., asolid tumor of a tissue selected from the group consisting of lung,colon, rectum, gall bladder, brain, breast, kidney, pancreas, stomach,liver, bone, skin, spleen, ovary, testis, prostate, and muscle.Preferably, the cancer is non-small cell lung cancer, a gastrointestinalstromal tumor, colorectal cancer, or renal cell carcinoma.

In some embodiments, the sample comprises a whole blood, serum, plasma,urine, nipple aspirate, lymph, saliva, fine needle aspirate, and/ortumor tissue sample. In certain instances, the whole blood sample isseparated into a plasma or serum fraction and a cellular fraction (i.e.,cell pellet). The circulating cells of the solid tumor can be isolatedfrom the cellular fraction using one or more of the separation methodsdescribed above. In some instances, the isolated circulating cells canbe stimulated in vitro with one or more growth factors before, during,and/or after incubation with one or more tyrosine kinase inhibitors ofinterest. In other instances, the isolated circulating cells can belysed, e.g., following growth factor stimulation, to produce a cellularextract (e.g., tumor cell lysate) using any technique known in the art.

In other embodiments, the tyrosine kinase inhibitor is selected from thegroup consisting of an EGFR inhibitor, VEGFR inhibitor, PDGFR inhibitor,c-KIT inhibitor, FLT-3 inhibitor, BCR-ABL inhibitor, and combinationsthereof. Examples of inhibitors belonging to each class are describedabove.

In another embodiment, the nucleic acid profile is selected from thegroup consisting of a genotypic profile, gene copy number profile, geneexpression profile, DNA methylation profile, and combinations thereof.In a further embodiment, the protein profile is selected from the groupconsisting of a protein expression profile, protein activation profile,and combinations thereof. Non-limiting examples of techniques that canbe used to determine these nucleic acid and protein profiles aredescribed above.

In some embodiments, the algorithm is used to calculate an index value.In certain instances, the index value comprises a cumulative indexvalue. Typically, the cumulative index value is compared to an indexcutoff value. In certain instances, a cumulative index value that isgreater than or equal to the index cutoff value indicates that thesubsequent dose of the tyrosine kinase inhibitor should be increased. Incertain other instances, a cumulative index value that is greater thanor equal to the index cutoff value indicates that the subsequent dose ofthe tyrosine kinase inhibitor should be decreased or an alternativecancer therapy should be administered. The method can further compriserecommending the subsequent dose (i.e., higher, lower, or the same) ofthe tyrosine kinase inhibitor to be administered or a dose of thealternative cancer therapy to be administered to the subject.

In some embodiments, recommending a subsequent dose of a tyrosine kinaseinhibitor is based upon determining at least one nucleic acid and/orprotein profile in conjunction with the use of a learning statisticalclassifier system. The learning statistical classifier system can beselected from the group consisting of a random forest (RF),classification and regression tree (C&RT), boosted tree, neural network(NN), support vector machine (SVM), general chi-squared automaticinteraction detector model, interactive tree, multiadaptive regressionspline, machine learning classifier, and combinations thereof.Preferably, the learning statistical classifier system is a tree-basedstatistical algorithm (e.g., RF, C&RT, etc.) and/or a neural network(e.g., artificial NN (ANN), etc.).

In certain instances, the algorithm comprises a single learningstatistical classifier system. In certain other instances, the algorithmcomprises a combination of at least two learning statistical classifiersystems. Preferably, the combination of learning statistical classifiersystems comprises a RF and a NN, e.g., used in tandem or parallel.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

In certain embodiments, the methods of the present invention furthercomprise sending the recommendation results to a clinician, e.g., anoncologist or a general practitioner.

IV. Tyrosine Kinase Inhibitors

Tyrosine kinase inhibitors represent a class of therapeutic agents ordrugs that target receptor and/or non-receptor tyrosine kinases in cellssuch as tumor cells. In certain instances, the tyrosine kinase inhibitoris an antibody-based (e.g., anti-tyrosine kinase monoclonal antibody,etc.) or polynucleotide-based (e.g., tyrosine kinase antisenseoligonucleotide, small interfering ribonucleic acid, etc.) form oftargeted therapy. Preferably, the tyrosine kinase inhibitor is a smallmolecule that inhibits target tyrosine kinases by binding to theATP-binding site of the enzyme. Examples of small molecule tyrosinekinase inhibitors include, but are not limited to, gefitinib (Iressa®),sunitinib (Sutent®; SU11248), erlotinib (Tarceva®; OSI-1774), lapatinib(GW572016; GW2016), canertinib (CI 1033), semaxinib (SU5416), vatalanib(PTK787/ZK222584), sorafenib (BAY 43-9006), imatinib (Gleevec®; STI571),dasatinib (BMS-354825), leflunomide (SU101), vandetanib (Zactima™;ZD6474), pharmaceutically acceptable salts thereof, derivatives thereof,analogs thereof, and combinations thereof. Additional examples oftyrosine kinase inhibitors suitable for use in the present inventioninclude quinazolines (e.g., PD 153035,4-(3-chloroanilino)quinazoline,etc.), pyridopyrimidines, pyrimidopyrimidines, pyrrolopyrimidines (e.g.,CGP 59326, CGP 60261, CGP 62706, etc.), pyrazolopyrimidines,4-(phenylamino)-7H-pyrrolo[2,3-d]pyrimidines, curcumin (diferuloylmethane), 4,5-bis(4-fluoroanilino)phthalimide, tyrphostines containingnitrothiophene moieties, quinoxalines (see, e.g., U.S. Pat. No.5,804,396), tryphostins (see, e.g., U.S. Pat. No. 5,804,396), PD0183805,PKI-166, EKB-569, IMC-1C11, Affinitac™ (LY900003; ISIS 3521), and thetyrosine kinase inhibitors described in PCT Publication Nos. WO99/09016, WO 98/43960, WO 97/38983, WO 99/06378, WO 99/06396, WO96/30347, WO 96/33978, WO 96/33979, and WO 96/33980.

As described herein, tyrosine kinase inhibitor therapy is generallylimited by low response rates, the development of acquired resistance,and/or toxic side-effects. As a result, tyrosine kinase inhibitorscurrently find use only as second- or third-line cancer therapies.However, the methods of the present invention for predicting oridentifying response and/or toxicity to tyrosine kinase inhibitors andmonitoring resistance to tyrosine kinase inhibitor therapyadvantageously enable tyrosine kinase inhibitors to be used in thefirst-line treatment of cancer.

Gefitinib (Iressa®) is a selective EGFR (HER1/ErbB1) tyrosine kinaseinhibitor, exhibiting a 200-fold greater affinity for EGFR than for HER2(Neu/ErbB2) (Thomas et al., Cancer Treat. Revs., 30:255-268 (2004)). Itprevents autophosphorylation of EGFR in a variety of tumor cell linesand xenografts (Arteaga et al., Curr. Opin. Oncol., 6:491-498 (2001)).Gefitinib can also inhibit the growth of some HER2-overexpressing tumorcells (e.g., breast cancer cells) (Moulder et al., Cancer Res.,61:8887-8895 (2001); Normanno et al., Ann. Oncol., 13:65-72 (2002)) andtumor neoangiogenesis (Arteaga et al., supra).

Gefitinib is currently approved for the treatment of patients withnon-small cell lung cancer after failure of both platinum-based anddocetaxel chemotherapies. However, most patients with non-small celllung cancer have no response to gefitinib. In fact, the response ratewas only about 10% in large scale Phase II trials of patients withrefractory disease (Fukuoka et al., J. Clin. Oncol., 21:2237-2246(2003); Kris et al., JAMA, 290:2149-2158 (2003)). Side-effects observedafter gefitinib administration are generally mild and resolve afterdiscontinuation of the drug. The most common adverse effects associatedwith gefitinib therapy include diarrhea, rash, acne, dry skin, nausea,vomiting, pruritus, anorexia, and asthenia (Dancey et al., Lancet,362:62-64 (2003)). Other toxic side-effects include fatigue, elevatedserum transaminase levels, stomatitis, bone pain, dyspnea, and pulmonarytoxicity such as interstitial lung disease (i.e., alveolitis),pneumonitis, and interstitial pneumonia (Cersosimo, Am. J. Health-Syst.Pharm., 61:889-898 (2004)).

Erlotinib (Tarceva®; OSI-1774) is another selective EGFR (HER1/ErbB1)tyrosine kinase inhibitor (Ranson, Br. J. Cancer, 90:2250-2255 (2004);Moyer et al., Cancer Res., 57:4838-4848 (1997)). It inhibitsEGF-dependent cell proliferation at nanomolar concentrations and blockscell cycle progression in the G1 phase (Moyer et al., supra). Erlotinibwas approved by the FDA in November, 2004. In a placebo-controlledtrial, patients randomized to erlotinib with advanced stage III or IVnon-small cell lung cancer and who had progressive disease afterstandard chemotherapies showed a low response rate of only 12% and amedian survival of 8.4 months (Perez-Soler, Clin. Cancer Res.,10:4238s-4240s (2004)). The most common side-effects observed witherlotinib include an acneiform skin rash and diarrhea. In fact, diarrheais a dose-limiting adverse event. Other side-effects include headache,mucositis, hyperbilirubinemia, neutropenia, and anemia (Ranson et al.,J. Clin. Oncol., 20:2240-2250 (2002); Ranson, Br. J. Cancer,90:2250-2255 (2004)).

Lapatinib (GW572016; GW2016) is a tyrosine kinase inhibitor of both EGFR(HER1/ErbB1) and HER2 (Neu/ErbB2). It has been shown to have activityagainst EGFR—, HER2-, and Akt-overexpressing human tumor xenografts(Rusnak et al., Mol. Cancer. Ther., 1:85-94 (2001)). In fact, itsnon-selective inhibition of several receptor tyrosine kinases mayaccount for a broader spectrum of antitumor activity and improvedefficacy, with a lower likelihood of developing resistance. The mostcommon side-effects observed with lapatinib include diarrhea and skinrash.

Canertinib (CI 1033) is a non-selective tyrosine kinase inhibitor thatproduces irreversible inhibition of all members of the EGFR family(Ranson, Br. J. Cancer, 90:2250-2255 (2004)). It has been shown to haveactivity against a variety of human breast carcinomas in tumor xenograftmodels (Allen et al., Semin. Oncol., 29:11-21 (2002)). However, onePhase II trial in patients with refractory ovarian cancer has revealedthat canertinib only possesses minimal antitumor activity (Campos etal., J. Clin. Oncol. ASCO Annual Meeting Proc., 22:5054 (2004)).

Sunitinib (Sutent®; SU11248) is a broad spectrum orally availablemulti-targeted tyrosine kinase inhibitor of VEGFR, PDGFR, c-KIT, andFLT-3 (Mendel et al., Proc. Am. Soc. Clin. Oncol., 21:94 (2002)). Itinhibits the growth of a variety of mouse tumor cells and xenograftmodels (Bergsland, Am. J. Health-Syst. Pharm., 61:S4-S11 (2004); Traxleret al., Cancer Res., 64:4931-4941 (2004)). Tumor regression andantiangiogenic activity have been observed in Phase I trials, and PhaseII studies in patients with metastatic kidney cancer have revealed that33% of patients had a partial response and 37% had stable disease forlonger than 3 months on sunitinib therapy (Eskens, Br. J. Cancer, 90:1-7(2004); Motzer et al., J. Clin. Oncol. ASCO Annual Meeting Proc.,22:4500 (2004)). Sunitinib has also been shown to delay the time oftumor progression and significantly reduce the death rate ofimatinib-resistant gastrointestinal stromal tumors (Demetri et al., J.Clin. Oncol. ASCO Annual Meeting Proc., 23:4000 (2005)).

Semaxinib (SU5416) is a non-selective tyrosine kinase inhibitor ofVEGFR-2, c-KIT, and FLT-3 (Mendel et al., Clin. Cancer Res., 6:4848-4858(2000)). In a multi-center Phase II trial with twice weeklyadministration of semaxinib, only 1 complete and 7 partial responseswere observed in patients with refractory acute myeloid leukemia(Fiedler et al., Blood, 102:2763-2767 (2003)). In addition, minimalobjective response rates were observed in Phase II studies of patientswith prostate cancer, renal cell carcinoma, or multiple myeloma. Toxicside-effects of semaxinib therapy include headache, nausea, vomiting,asthenia, pain at the infusion site, phlebitis, change in voice, andfever.

Vatalanib (PTK787/ZK222584) is a selective inhibitor of VEGF-1 (FLT-1)and VEGFR-2 (FLK-1/KDR). At higher concentrations, it also inhibitsother tyrosine kinases such as PDGFR-β, c-KIT, and C-FMS (Lin et al.,Cancer Res., 2:5019-5026 (2002)). Studies on vatalanib have focused onits use in treating colorectal cancer, liver cancer, advanced prostatecancer, advanced renal cell carcinoma, and relapsed/refractoryglioblastoma (Steward et al., Proc. Am. Soc. Clin. Oncol., 22:1098(2003); George et al., Clin. Cancer Res., 7:548 (2001); Bergsland, Am.J. Health-Syst. Pharm., 61:S4-S11 (2004)). However, partial and minorresponses to vatalanib were observed in only 5% and 15% of patients withrenal cell carcinoma, respectively (Rini et al., J. Clin. Oncol.,23:1028-1043 (2005)). Toxic side-effects of vatalanib therapy includeataxia, vertigo, hypertension, and venous thromboembolism (Eskens, Br.J. Cancer, 90:1-7 (2004)).

Sorafenib (BAY 43-9006) is a RAF kinase and VEGFR, EFGR, and PDGFRtyrosine kinase inhibitor that blocks tumor cell proliferation andangiogenesis (Wilhelm et al., Cancer Res., 64:7099-7109 (2004);Strumberg et al., J. Clin. Oncol., 23:965-972 (2005)). It hassignificant activity in renal, colon, pancreatic, lung, and ovariantumors (Wilhelm et al., supra). A Phase II randomized clinical trial inpatients with advanced kidney cancer showed a statistically higherpercentage of patients whose disease did not progress after a 12-weektreatment period with sorafenib compared to the placebo group (Ratain etal., J. Clin. Oncol. ASCO Annual Meeting Proc., 22:4501 (2004)). Themost common side-effects of sorafenib therapy include skin reactionssuch as hand-foot syndrome and rash, diarrhea, fatigue, weight loss, andhypertension.

Imatinib (Gleeve®; STI571) is an inhibitor of the ABL, BCR-ABL, c-KIT,and PDGFR tyrosine kinases (Druker et al., Nat. Med., 5:561-566 (1996)).It is used for the treatment of Philadelphia chromosome-positivepatients with chronic myeloid leukemia who are either newly diagnosed orhave failed interferon-α therapy (Kantarjian et al., N. Engl. J. Med.,346:645-652 (2002); Druker et al., N. Engl. J. Med., 344:1038-1042(2001)). For example, imatinib therapy induced major cytogeneticresponses in patients with chronic myeloid leukemia and is alsoeffective in the treatment of adult acute lymphoblastic leukemia(Kantarjian et al., Clin. Cancer Res., 8:2177-2187 (2002); Druker etal., N. Engl. J. Med., 344:1038-1042 (2001)). In some patients, however,white blood cells become resistant to imatinib, resulting in relapse.Several clinical trials have also shown a significant response toimatinib in patients with advanced gastrointestinal stromal tumors(Druker, Adv. Cancer Res., 91:1-35 (2004)). In fact, imatinib is nowapproved for the treatment of patients with c-KIT-positive unresectableand/or malignant gastrointestinal stromal tumors. Toxic side-effectsassociated with imatinib therapy include neutropenia, thrombocytopenia,anemia, nausea, skin rash, peripheral edema, muscle cramps, and elevatedliver transaminase levels (Kantarjian et al., N. Engl. J. Med.,346:645-652 (2002)).

Leflunomide (SU101) is a small molecule inhibitor of PDGFR-mediatedphosphorylation and thus inhibits PDGF-mediated cell signaling (Shawveret al., Clin. Cancer Res., 3:1167-1177 (1997)). A Phase II study inpatients with hormone refractory prostate cancer indicated thatadministration of leflunomide resulted in partial responses in less than5% of patients and a decrease in prostate specific antigen of greaterthan 50% in only about 7% of patients (Ko et al., Clin. Cancer Res.,4:800-805 (2001)). The most common side-effects include asthenia,nausea, anorexia, and anemia.

Although the dose of a tyrosine kinase inhibitor administered to apatient varies with the cancer being treated, the dose should generallybe between about 1 mg/day to about 800 mg/day, and preferably, betweenabout 100 mg/day to about 400 mg/day. For example, the recommended doseof orally administered gefitinib for patients with non-small cell lungcancer is between about 200 mg/day to about 300 mg/day, and preferablyabout 250 mg/day. As another example, the recommended dose of orallyadministered sunitinib for patients with gastrointestinal stromal tumorsor renal cell carcinoma is between about 20 mg/day to about 100 mg/day,and preferably about 50 mg/day. Higher doses may be required in patientswith more advanced tumors. Doses can be given at any time of the day,with or without food. Adjustments of dosage, if necessary, can be madeaccording to the methods of the present invention to optimizetherapeutic efficacy and/or reduce toxicity. In particular, the methodsof the present invention provide algorithms useful for determiningwhether a subsequent dose of a tyrosine kinase inhibitor should beincreased or decreased in order to reach a therapeutic threshold and/orminimize toxicity (e.g., side-effects). The methods of the presentinvention also provide algorithms useful for determining whether asuitable dose of an alternative cancer therapy should be administereddue to the development of resistance to tyrosine kinase inhibitortherapy.

V. Profiles

The present invention provides assay methods for predicting, monitoring,or optimizing tyrosine kinase inhibitor therapy in a subject using analgorithmic approach by determining at least one nucleic acid and/orprotein profile in a sample from the subject. Examples of nucleic acidprofiles include, but are not limited to, a genotypic profile, gene copynumber profile, gene expression profile, DNA methylation profile, andcombinations thereof. Non-limiting examples of protein profiles includea protein expression profile, protein activation profile, andcombinations thereof. Nucleic acid profiling typically comprisesanalyzing one or more genetic biomarkers, while protein profilinggenerally comprises analyzing one or more biochemical or serologicalbiomarkers.

Several biomarkers may be combined into one test for efficientprocessing of multiple samples. In addition, one of skill in the artwould recognize the value of testing multiple samples (e.g., atsuccessive time points, before and after administration of a tyrosinekinase inhibitor, etc.) from the same subject. Such testing of serialsamples can allow the identification of changes in biomarker levels overtime. Increases or decreases in biomarker levels, as well as the absenceof change in biomarker levels, can provide useful information to createa specific tyrosine kinase inhibitor dosing regimen for a subjectdiagnosed with cancer by determining the initial and/or subsequent dosesof the drug that should be administered to the subject.

A panel consisting of one or more of the biomarkers described herein maybe constructed to provide relevant information related to predicting,identifying, or monitoring efficacy and/or toxicity to tyrosine kinaseinhibitor therapy. Such a panel may be constructed using 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40,45, 50, or more individual biomarkers. The analysis of a singlebiomarker or subsets of biomarkers can also be carried out by oneskilled in the art to optimize dose efficacy or reduce toxicity totyrosine kinase inhibitor therapy in various clinical settings. Theseinclude, but are not limited to, ambulatory, urgent care, critical care,intensive care, monitoring unit, inpatient, outpatient, physicianoffice, medical clinic, and health screening settings.

The analysis of biomarkers could be carried out in a variety of physicalformats as well. For example, the use of microtiter plates or automationcould be used to facilitate the processing of large numbers of testsamples. Alternatively, single sample formats could be developed tofacilitate diagnosis, prognosis, and/or treatment in a timely fashion.

A. Genotypic Profiling

A variety of techniques can be used for genotypic analysis of apolymorphic site in determining a genotypic profile according to themethods of the present invention. For example, enzymatic amplificationof nucleic acid from a sample can be conveniently used to obtain nucleicacid for subsequent analysis. However, the presence or absence of avariant allele can also be determined directly from a nucleic acidsample without enzymatic amplification (e.g., using hybridizationtechniques). Genotyping of nucleic acid, whether amplified or not, canbe performed using any of various techniques known to one of skill inthe art. Useful techniques include, without limitation, polymerase chainreaction (PCR)-based analysis, sequence analysis, and electrophoreticanalysis, which can be used alone or in combination.

A nucleic acid sample can be obtained from a subject using routinemethods. Such samples comprise any biological matter from which nucleicacid can be prepared. As non-limiting examples, suitable samples includewhole blood, serum, plasma, saliva, cheek swab, urine, or other bodilyfluid or tissue that contains nucleic acid. In one embodiment, themethods of the present invention are performed using whole blood orfractions thereof such as serum or plasma, which can be obtained readilyby non-invasive means and used to prepare genomic DNA. In anotherembodiment, genotyping involves the amplification of a subject's nucleicacid using PCR. Use of PCR for the amplification of nucleic acids iswell known in the art (see, e.g., Mullis et al., The Polymerase ChainReaction, Birkhäuser, Boston, (1994)). In yet another embodiment, PCRamplification is performed using one or more fluorescently labeledprimers. In a further embodiment, PCR amplification is performed usingone or more labeled or unlabeled primers containing a DNA minor grovebinder. Generally, protocols for the use of PCR in identifying mutationsand polymorphisms in a gene of interest are described in Theophilus etal., “PCR Mutation Detection Protocols,” Humana Press (2002). Furtherprotocols are provided in Innis et al., “PCR Applications: Protocols forFunctional Genomics,” 1st Edition, Academic Press (1999).

Any of a variety of different primers can be used to PCR amplify asubject's nucleic acid. One skilled in the art understands that primersfor PCR analysis can be designed based on the sequence flanking thepolymorphic site of interest. As a non-limiting example, a PCR primercan contain between about 15 to about 60 nucleotides (e.g., 15-50,15-40, or 15-30 nucleotides) of a sequence upstream or downstream of thepolymorphic site of interest. Such primers generally are designed tohave sufficient guanine and cytosine content to attain a high meltingtemperature which allows for a stable annealing step in theamplification reaction. Several computer programs, such as PrimerSelect, are available to aid in the design of PCR primers.

Primer sequences and amplification protocols for evaluating EGFR(HER1/ErbB1) mutations are known to those in the art and have beenpublished in, e.g., Lynch et al., New Eng. J. Med., 350:2129-2139(2004); Paez et al., Science, 304:1497-1500 (2004); Pao et al., Proc.Natl. Acad. Sci., 101:13306-13311 (2004); and Pao et al., PLoS Med.,2:57-61 (2005). Preferably, the subject is genotyped to determine thepresence or absence of an activating mutation (i.e., gain-of-functionallele) in the tyrosine kinase domain of the EGFR gene. Such mutationsinclude, but are not limited to, a T→G mutation at nucleotide 2573 inthe EGFR gene, which results in a substitution of arginine for leucineat position 858 (L858R); a T→A mutation at nucleotide 2582 in the EGFRgene, which results in a substitution of glutamine for leucine atposition 861 (L858Q); a G→T mutation at nucleotide 2155 in the EGFRgene, which results in a substitution of cysteine for glycine atposition 719 (G719C); a deletion of nucleotides 2235-2249 in the EGFRgene, which results in an in-frame deletion of amino acids 746-750; adeletion of nucleotides 2240-2251 in the EGFR gene, which results in anin-frame deletion of amino acids 747-751 and the insertion of a serine;and a deletion of nucleotides 2240-2257 in the EGFR gene, which resultsin an in-frame deletion of amino acids 747-753 and the insertion of aserine. Other deletions, insertions, and/or single nucleotidesubstitutions in exons 18, 19, and/or 21 of the EGFR gene can also bedetermined according to the methods of the present invention.Alternatively, the subject can be genotyped to determine the presence orabsence of a loss-of-function mutation in the EGFR gene.

Primer sequences and amplification protocols for evaluating K-Rasmutations are known to those in the art and have been published in,e.g., Pao et al., PLoS Med., 2:57-61 (2005). Preferably, the subject isgenotyped to determine the presence or absence of an activating mutationin the K-Ras gene. Such mutations include, but are not limited to, a G→Tmutation at nucleotide 34 in the K-Ras gene, which results in asubstitution of cysteine for glycine at position 12 (G12C); a G→Tmutation at nucleotide 37 in the K-Ras gene, which results in asubstitution of cysteine for glycine at position 13 (G13C); a G→Amutation at nucleotide 35 in the K-Ras gene, which results in asubstitution of aspartic acid for glycine at position 12 (G12D); a G→Amutation at nucleotide 34 in the K-Ras gene, which results in asubstitution of serine for glycine at position 12 (G12S); and a G→Tmutation at nucleotide 35 in the K-Ras gene, which results in asubstitution of valine for glycine at position 12 (G12V). Alternatively,the subject can be genotyped to determine the presence or absence of aloss-of-function mutation in the K-Ras gene.

Primer sequences and amplification protocols for evaluating c-KITmutations are known to those in the art and have been published in,e.g., Corless et al., J. Mol. Diagn., 6:366-70 (2004); Hirota et al.,Science, 279:577-580 (1998); Hirota et al., J. Pathol., 193:505-510(2001); Antonescu et al., Clin. Cancer Res., 9:3329-3337 (2003); Emileet al., Diagn. Mol. Pathol., 11:107-112 (2002); Lasota et al., Am. J.Pathol., 157:1091-1095 (2000); Lee et al., Am. J. Surg. Pathol.,25:979-987 (2001); Lux et al., Am. J. Pathol., 156:791-795 (2000); andTaniguchi et al., Cancer Res., 59:4297-4300 (1999). Preferably, thesubject is genotyped to determine the presence or absence of anactivating mutation in the c-KIT gene. Such mutations include, but arenot limited to, any of a variety of deletions, insertions, and/or singlenucleotide substitutions in exons 9, 11, 13, and/or 17 of the c-KITgene. Alternatively, the subject can be genotyped to determine thepresence or absence of a loss-of-function mutation in the c-KIT gene.

Primer sequences and amplification protocols for evaluating PDGFRAmutations are known to those in the art and have been published in,e.g., Hirota et al., Pathol. Int., 56:1-9 (2006); Corless et al., J.Clin. Oncol., 23:5357-5364 (2005); and Penzel et al., J. Clin. Pathol.,58:634-639 (2005). Preferably, the subject is genotyped to determine thepresence or absence of an activating mutation in the PDGFRA gene. Suchmutations include, but are not limited to, any of a variety ofdeletions, insertions, and/or single nucleotide substitutions in exons12, 14, and/or 18 of the PDGFRA gene. Alternatively, the subject can begenotyped to determine the presence or absence of a loss-of-functionmutation in the PDGFRA gene.

Primer sequences and amplification protocols for evaluating VEGFR-1(FLT-1) mutations are known to those in the art and have been publishedin, e.g., Meshinchi et al., Blood, 102:1474-1479 (2003). Preferably, thesubject is genotyped to determine the presence or absence of anactivating mutation in the VEGFR-1 gene. Such mutations include, but arenot limited to, any of a variety of deletions, insertions, and/or singlenucleotide substitutions in the juxtamembrane domain or tyrosine kinasedomain of the VEGFR-1 gene. Alternatively, the subject can be genotypedto determine the presence or absence of a loss-of-function mutation inthe VEGFR-1 gene.

Primer sequences and amplification protocols for evaluating VEGFR-2(FLK-1/KDR) mutations are known to those in the art and have beenpublished in, e.g., Walter et al., Genes Chromosomes Cancer, 33:295-303(2002); and Meshinchi et al., Blood, 102:1474-1479 (2003). Preferably,the subject is genotyped to determine the presence or absence of anactivating mutation in the VEGFR-2 gene. Such mutations include, but arenot limited to, any of a variety of deletions, insertions, and/or singlenucleotide substitutions in the juxtamembrane domain or tyrosine kinasedomain of the VEGFR-2 gene, such as a missense mutation (P1147S) in thekinase domain. Alternatively, the subject can be genotyped to determinethe presence or absence of a loss-of-function mutation in the VEGFR-2gene.

Primer sequences and amplification protocols for evaluating VEGFR-3(FLT-4) mutations are known to those in the art and have been publishedin, e.g., Walter et al., Genes Chromosomes Cancer, 33:295-303 (2002).Preferably, the subject is genotyped to determine the presence orabsence of an activating mutation in the VEGFR-3 gene. Such mutationsinclude, but are not limited to, any of a variety of deletions,insertions, and/or single nucleotide substitutions in the juxtamembranedomain or tyrosine kinase domain of the VEGFR-3 gene, such as a missensemutation (P954S) in the kinase domain. Alternatively, the subject can begenotyped to determine the presence or absence of a loss-of-functionmutation in the VEGFR-3 gene.

Primer sequences and amplification protocols for evaluating FLT-3(FLK-2) mutations are known to those in the art and have been publishedin, e.g., Gilliland et al., Curr. Opin., Hematol., 9:274-281 (2002); andGilliland et al., Blood, 100:1532-1542 (2002). Preferably, the subjectis genotyped to determine the presence or absence of an activatingmutation in the FLT-3 gene. Such mutations include, but are not limitedto, an internal tandem duplication in the juxtamembrane domain of theFLT-3 gene; and an activating loop mutation in the tyrosine kinasedomain of the FLT-3 gene, which results in a substitution of asparticacid for another amino acid at position 835 (D835X). Alternatively, thesubject can be genotyped to determine the presence or absence of aloss-of-function mutation in the FLT-3 gene.

A Taqman® allelic discrimination assay available from Applied Biosystems(Foster City, Calif.) can be useful for genotypic analysis of apolymorphic site to determine the presence or absence of a variantallele. In a Taqman® allelic discrimination assay, a specific,fluorescent dye-labeled probe for each allele is constructed. The probescontain different fluorescent reporter dyes such as FAM and VIC todifferentiate the amplification of each allele. In addition, each probehas a quencher dye at one end which quenches fluorescence byfluorescence resonance energy transfer. During PCR, each probe annealsspecifically to complementary sequences in the nucleic acid from thesubject. The 5′ nuclease activity of Taq polymerase is used to cleaveonly probe that hybridizes to the allele. Cleavage separates thereporter dye from the quencher dye, resulting in increased fluorescenceby the reporter dye. Thus, the fluorescence signal generated by PCRamplification indicates which alleles are present in the sample.Mismatches between a probe and allele reduce the efficiency of bothprobe hybridization and cleavage by Taq polymerase, resulting in littleto no fluorescent signal. Those skilled in the art understand thatimproved specificity in allelic discrimination assays can be achieved byconjugating a DNA minor grove binder (MGB) group to a DNA probe asdescribed, e.g., in Kutyavin et al., Nuc. Acids Res., 28:655-661 (2000).Suitable minor grove binders for use in the present invention include,but are not limited to, compounds such as dihydrocyclopyrroloindoletripeptide (DPI3).

Sequence analysis can also be useful for genotyping at a polymorphicsite in a gene. In one embodiment, a variant allele can be detected bysequence analysis using the appropriate primers, which are designedbased on the sequence flanking the polymorphic site of interest, as isknown by those skilled in the art. As a non-limiting example, asequencing primer can contain between about 15 to about 60 nucleotides(e.g., 15-50, 15-40, or 15-30 nucleotides) of a sequence between about40 to about 400 base pairs upstream or downstream of the polymorphicsite of interest. Such primers are generally designed to have sufficientguanine and cytosine content to attain a high melting temperature whichallows for a stable annealing step in the sequencing reaction.

As used herein, the term “sequence analysis” includes any manual orautomated process by which the order of nucleotides in a nucleic acid isdetermined. As an example, sequence analysis can be used to determinethe nucleotide sequence of a sample of DNA. The term encompasses,without limitation, chemical and enzymatic methods such as dideoxyenzymatic methods including, for example, Maxam-Gilbert and Sangersequencing as well as variations thereof. The term also encompasses,without limitation, capillary array DNA sequencing, which relies oncapillary electrophoresis and laser-induced fluorescence detection andcan be performed using instruments such as the MegaBACE 1000 or ABI3700. As additional non-limiting examples, the term encompasses thermalcycle sequencing (Sears et al., Biotechniques, 13:626-633 (1992));solid-phase sequencing (Zimmerman et al., Methods Mol. Cell. Biol.,3:39-42 (1992); and sequencing with mass spectrometry, such asmatrix-assisted laser desorption/ionization time-of-flight massspectrometry (MALDI-TOF MS; Fu et al., Nature Biotech., 16:381-384(1998)). The term further includes, without limitation, sequencing byhybridization (SBH), which relies on an array of all possible shortoligonucleotides to identify a segment of sequence (Chee et al.,Science, 274:610-614 (1996); Drmanac et al., Science, 260:1649-1652(1993); Drmanac et al., Nature Biotech., 16:54-58 (1998)). One skilledin the art understands that these and additional variations areencompassed by the term as defined herein. See, in general, Ausubel etal., Current Protocols in Molecular Biology, Chapter 7 and Supplement47, John Wiley & Sons, Inc., New York (1999).

In addition, electrophoretic analysis can be useful for genotyping at apolymorphic site in a gene. The term “electrophoretic analysis,” as usedherein in reference to one or more nucleic acids such as amplifiedfragments, includes a process whereby charged molecules are movedthrough a stationary medium under the influence of an electric field.Electrophoretic migration separates nucleic acids primarily on the basisof their charge, which is in proportion to their size, with smallermolecules migrating more quickly. The term includes, without limitation,analysis using slab gel electrophoresis such as agarose orpolyacrylamide gel electrophoresis, or capillary electrophoresis.Capillary electrophoretic analysis generally occurs inside asmall-diameter quartz capillary in the presence of high (kilovolt-level)separating voltages with separation times of a few minutes. Usingcapillary electrophoretic analysis, nucleic acids are convenientlydetected by UV absorption or fluorescent labeling, and single-baseresolution can be obtained on fragments up to several hundred base pairsin length. Such methods of electrophoretic analysis, and variationsthereof, are well known in the art, as described, for example, inAusubel et al., Current Protocols in Molecular Biology, Chapter 2 andSupplement 45, John Wiley & Sons, Inc., New York (1999).

Restriction fragment length polymorphism (RFLP) analysis can also beuseful for genotypic analysis of a polymorphic site in a gene (see,e.g., Jarcho et al., Current Protocols in Human Genetics, pages2.7.1-2.7.5, John Wiley & Sons, Inc., New York; Innis et al., PCRProtocols, San Diego, Academic Press, Inc. (1990)). As used herein,“restriction fragment length polymorphism analysis” includes any methodfor distinguishing polymorphic alleles using a restriction enzyme, whichis an endonuclease that catalyzes degradation of nucleic acid followingrecognition of a specific base sequence, generally a palindrome orinverted repeat. One skilled in the art understands that the use of RFLPanalysis depends upon an enzyme that can differentiate a variant allelefrom a wild-type or other allele at a polymorphic site.

Furthermore, allele-specific oligonucleotide hybridization can be usefulfor genotyping at a polymorphic site in a gene. Allele-specificoligonucleotide hybridization is based on the use of a labeledoligonucleotide probe having a sequence perfectly complementary, forexample, to the sequence encompassing the variant allele. Underappropriate conditions, the variant allele-specific probe hybridizes toa nucleic acid containing the variant allele but does not hybridize tothe one or more other alleles, which have one or more nucleotidemismatches as compared to the probe. If desired, a secondallele-specific oligonucleotide probe that matches an alternate (e.g.,wild-type) allele can also be used. Similarly, the technique ofallele-specific oligonucleotide amplification can be used to selectivelyamplify, for example, a variant allele by using an allele-specificoligonucleotide primer that is perfectly complementary to the nucleotidesequence of the variant allele but which has one or more mismatches ascompared to other alleles (Mullis et al., The Polymerase Chain Reaction,Birkhäuser, Boston, (1994)). One skilled in the art understands that theone or more nucleotide mismatches that distinguish between the variantallele and other alleles are often located in the center of anallele-specific oligonucleotide primer to be used in the allele-specificoligonucleotide hybridization. In contrast, an allele-specificoligonucleotide primer to be used in PCR amplification generallycontains the one or more nucleotide mismatches that distinguish betweenthe variant allele and other alleles at the 3′ end of the primer.

A heteroduplex mobility assay (HMA) is another well-known assay that canbe used for genotyping at a polymorphic site in a gene. HMA is usefulfor detecting the presence of a variant allele since a DNA duplexcarrying a mismatch has reduced mobility in a polyacrylamide gelcompared to the mobility of a perfectly base-paired duplex (see, e.g.,Delwart et al., Science, 262:1257-1261 (1993); White et al., Genomics,12:301-306 (1992)).

The technique of single strand conformational polymorphism (SSCP) canalso be useful for genotypic analysis of a polymorphic site in a geneaccording to the methods of the present invention (see, e.g., Hayashi,Methods Applic., 1:34-38 (1991)). This technique is used to detectvariant alleles based on differences in the secondary structure ofsingle-stranded DNA that produce an altered electrophoretic mobilityupon non-denaturing gel electrophoresis. Variant alleles are detected bycomparison of the electrophoretic pattern of the test fragment tocorresponding standard fragments containing known alleles.

Denaturing gradient gel electrophoresis (DGGE) is another usefultechnique for genotyping at a polymorphic site in a gene. In DGGE,double-stranded DNA is electrophoresed in a gel containing an increasingconcentration of denaturant. Because double-stranded fragmentscomprising mismatched alleles have segments that melt more rapidly, suchfragments migrate differently as compared to perfectly complementarysequences (Sheffield et al., “Identifying DNA Polymorphisms byDenaturing Gradient Gel Electrophoresis,” in Innis et al., PCRProtocols, San Diego, Academic Press, Inc. (1990)).

Other molecular techniques useful for genotypic analysis of apolymorphic site in a gene are also known in the art and useful in themethods of the present invention. Other well-known genotyping techniquesinclude, without limitation, automated sequencing and RNAase mismatchtechniques (Winter et al., Proc. Natl. Acad. Sci., 82:7575-7579 (1985)).Furthermore, one skilled in the art understands that, where the presenceor absence of multiple variant alleles is to be determined, individualvariant alleles can be detected by any combination of moleculartechniques. See, in general, Birren et al., Genome Analysis: ALaboratory Manual, Volume 1 (Analyzing DNA), New York, Cold SpringHarbor Laboratory Press (1997). In addition, one skilled in the artunderstands that multiple variant alleles can be detected in individualreactions or in a single reaction, e.g., using a multiplex real-time PCRassay. Kits for performing multiplex real-time PCR of cDNA or genomicDNA targets using sequence-specific probes are available from QIAGENInc. (Valencia, Calif.), e.g., the QuantiTect Multiplex PCR Kit. Systemsfor performing multiplex real-time PCR are available from AppliedBiosystems (Foster City, Calif.), e.g., the 7300 or 7500 Real-Time PCRSystems.

In view of the above, one skilled in the art will readily appreciatethat the methods of the present invention for determining a genotypicprofile in a sample can be practiced using one or any combination of thewell-known techniques described above or other techniques known in theart.

B. Gene Expression Profiling

A gene expression profile is typically evaluated in vitro on a samplecollected from a subject in comparison to a normal or reference sample.Determination of a transcriptional expression profile can beaccomplished, e.g., using hybridization techniques well-known to thoseskilled in the art such as Northern analysis and slot blot hybridizationor by performing reverse-transcriptase (RT)-PCR amplification followedby gel electrophoresis. Applicable PCR amplification techniques aredescribed in Ausubel et al., Current Protocols in Molecular Biology,John Wiley & Sons, Inc., New York (1999); Theophilus et al., “PCRMutation Detection Protocols,” Humana Press (2002); and Innis et al.,“PCR Applications: Protocols for Functional Genomics,” 1st Edition,Academic Press (1999). General nucleic acid hybridization methods aredescribed in Anderson, “Nucleic Acid Hybridization,” BIOS ScientificPublishers (1999). Amplification or hybridization of a plurality oftranscribed nucleic acid sequences (e.g., mRNA or cDNA) can also beperformed using mRNA or cDNA sequences arranged in a microarray.Microarray methods are generally described in Hardiman, “MicroarraysMethods and Applications: Nuts & Bolts,” DNA Press (2003) and Baldi etal., “DNA Microarrays and Gene Expression: From Experiments to DataAnalysis and Modeling,” Cambridge University Press (2002).

Comparing patterns of gene expression is a widely used means ofidentifying novel genes, investigating gene function, and findingpotential new therapeutic targets (Shiue et al., Drug Devel. Res.,41:142-159 (1997)). Many techniques have been used to identify and clonedifferentially expressed genes (Liang et al., Science, 257:967-971(1992); Welsh et al., Nucleic Acids Res., 20:4965-4970 (1992); Tedder etal., Proc. Natl. Acad. Sci., 85:208-212 (1988); Davis et al., Proc.Natl. Acad. Sci., 81:2194-2198 (1984); Lisitsyn et al., Science,259:946-951 (1993); Velculescu et al., Science, 270:484-487 (1995);Diatchenko et al., Proc. Natl. Acad. Sci., 93:6025-6030 (1996); Jiang etal., Proc. Natl. Acad. Sci., 97:12684-12689 (2000); Yang et al., NucleicAcids Res., 27: 517-523 (1999)).

Recently, it has become routine to use the technique of cDNA microarrayhybridization to quantify the expression of many thousands of discretemRNA or cDNA sequences in a single assay known as expression profiling(van't Veer et al., Nature, 415:530-536 (2002); Hughes et al., NatureBiotech., 19:342-347 (2001); Hughes et al., Cell, 102:109-126 (2000);Lockhart and Winzeler, Nature, 405:827-836 (2000); Roberts et al.,Science, 287:873-880 (2000); Wang et al., Gene, 229:101-108 (1999);Lockhart et al., Nat. Biotech., 14:1675-1680 (1996); Schena et al.,Science, 270:467-470 (1995); U.S. Pat. No. 6,040,138). For example, EGFRmRNA levels can be measured in tumor samples by microarray hybridizationas described in Bhargava et al., Mod. Pathol., 18:1027-1033 (2005). Incertain embodiments, a gene expression microarray groups genes accordingto similarities in patterns of gene expression in expression profilingexperiments.

In addition, gene expression profiles can be used to identifypathway-specific reporters and target genes for a particular biologicalpathway of interest. Such reporter genes and probes directed to them canbe used to measure the activity of a particular biological pathway andmay be further used in the design of drugs, drug therapies, or otherbiological agents to target a particular biological pathway. Geneexpression profiles can also be used to determine protein activitylevels of a target protein using the methods described in U.S. Pat. No.6,324,479.

The measurement of gene expression profiles using microarrays also hasmany important applications to the monitoring of disease states andtherapies (see, e.g., U.S. Pat. Nos. 6,218,122 and 6,222,093), theidentification of drug targets, the identification of pathways of drugaction, and drug design (see, e.g., U.S. Pat. Nos. 6,303,291, 6,165,709,6,146,830, 5,965,352, and 5,777,888). For example, van't Veer et al.,supra, identified “good prognosis” and “poor prognosis” gene expressionsignatures that could be used to predict the clinical outcome of breastcancer patients. Similarly, U.S. Pat. No. 5,777,888 discloses theutility of microarray gene expression profiles to evaluate the targetspecificity of a candidate drug by comparison of an expression profileobtained from cells treated with the candidate drug to a database ofexpression profiles obtained from cells treated with known drugs. U.S.Pat. No. 6,218,122 provides methods for monitoring the disease state ofa subject and determining the effect of a therapy upon the subjectthrough the use of gene expression profiles (see, also, U.S. Pat. No.6,266,093). In addition, Shoemaker et al., Nature, 409:922-927 (2000),discloses methods for using microarray gene expression profiles todetect splice variants.

In view of the above, one skilled in the art will readily appreciatethat the methods of the present invention for determining a geneexpression profile from a sample of a subject can be practiced using oneor any combination of the well-known techniques described above or othertechniques known in the art.

C. Gene Copy Number Profiling

Analysis of biomarker gene amplification levels can also be used aloneor in combination with other markers to predict, monitor, or optimizetyrosine kinase inhibitor therapy in a subject. Any method known in theart for detecting or determining a level of gene amplification of one ormore of the biomarkers described herein is suitable for use in thepresent invention.

In some embodiments, the level of gene amplification of a biomarker canbe determined by DNA-based techniques such as PCR or Southern blotanalysis or by molecular cytogenetic techniques such as fluorescence insitu hybridization (FISH), chromogenic in situ hybridization (CISH), andimmunohistochemistry (IHC). For example, the level of EGFR geneamplification in cancer cells can be determined using FISH as describedin Cappuzzo et al., J. Natl. Caner Inst., 97:643-655 (2005). Similarly,the level of HER2 gene amplification in cancer cells can be determinedusing FISH as described in Cappuzzo et al., J. Clin. Oncol.,23:5007-5018 (2005). Likewise, the level of c-KIT, PDGFRA, and/or VEGFR2gene amplification in cancer cells can be determined using FISH asdescribed in Joensuu et al., J. Pathol., 207:224-231 (2005). EGFR genecopy number can also be determined using real-time quantitative PCR asdescribed in Bell et al., J. Clin. Oncol., 23:8081-8092 (2005) or CISHas described in Bhargava et al., Mod. Pathol., 18:1027-1033 (2005).Other techniques include genome-wide scanning of amplified chromosomalregions with comparative genomic hybridization for the detection ofamplified regions in tumor DNA (see, e.g., Kallioniemi et al., Science,258:818-821 (1992)) and the detection of gene amplification by genomichybridization to cDNA microarrays (see, e.g., Heiskanen et al., CancerRes., 60:799-802 (2000)). One skilled in the art will know of additionalgene amplification techniques that can be used to detect or determine alevel of an amplified gene that corresponds to a biomarker of thepresent invention.

D. DNA Methylation Profiling

Analysis of biomarker DNA methylation levels can also be used alone orin combination with other markers to predict, monitor, or optimizetyrosine kinase inhibitor therapy in a subject.

The regulation of gene expression by epigenetic mechanisms such asmethylation contributes to various biological processes includinggenomic imprinting, X-chromosomal inactivation, cellulardifferentiation, and aging, as well as the development of malignantdiseases such as cancer (see, e.g., Ferguson-Smith et al., Science,293:1086-1089 (2001); Lee, Curr. Biol., 13:R242-254 (2003); Issa, Clin.Immunol., 109:103-108 (2003); and Robertson, Nat. Rev. Genet., 6:597-610(2005)). In mammals, methylation of DNA typically occurs at specificcytosine residues which precede a guanosine residue (i.e., CpGdinucleotides) and generally correlates with stable transcriptionalrepression (see, e.g., Bestor, Hum. Mol. Genet., 9:2395-2402 (2000); Nget al., Curr. Opin. Genet. Dev., 9:158-163 (1999); and Razin, EMBO J.,17, 4905-4908 (1998)). The aberrant gain of DNA methylation (i.e.,hypermethylation) in neoplastic cells frequently affects DNA sequenceswith a relatively high content of CpG dinucleotides, known as CpGislands. These regions often contain transcription initiation sites andpromoters and are generally not methylated in noijual cells (see, e.g.,Costello et al., J. Med. Genet., 38:285-303 (2001); Tycko, Mutat. Res.,386:131-140 (1997); and Wolffe et al., Proc. Natl. Acad. Sci. USA,96:5894-5896 (1999)). However, hypermethylation of CpG islands causestranscriptional repression and, in cancer, leads to the abnormalsilencing of genes such as tumor suppressor genes (see, e.g., Estelleret al., Science, 297:1807-1808 (2002); Herman et al., N. Engl. J. Med.,349:2042-2054 (2003); Momparler, Oncogene, 22:6479-6483 (2003); andPlass, Hum. Mol. Genet., 11:2479-2488 (2002)). As a result, analyzingthe level of DNA methylation in, e.g., the genomic regulatory sequencesof biomarkers such as tumor suppressor genes (e.g., PTEN, DMBT1, LGI1,p53, ESR1, CDKN2B, ICSBP, ETV3, DDX20, etc.) can be useful in themethods of the present invention.

Any technique known in the art can be used for detecting or determiningthe CpG methylation state of one or more of the biomarkers describedherein. For example, the level of DNA methylation of a biomarker can bedetermined by chromatographic separation, use of methylation-sensitiverestriction enzymes, and bisulfite-driven conversion of non-methylatedcytosine to uracil (see, e.g., Ushijima, Nat. Rev. Cancer, 5:223-231(2005)). Biomarker DNA methylation levels can also be determined by asystem designed for the application of immunofluorescence using amonoclonal antibody that specifically recognizes 5′-methyl-cytosineresidues in single-stranded DNA hybridized to oligonucleotidemicroarrays (see, e.g., Pröll et al., DNA Res., 13:37-42 (2006)).Alternatively, the level of DNA methylation of a biomarker can bedetermined using the methyl-binding PCR technique described in Gebhardet al., Nuc. Acids Res., 34:e82 (2006). One skilled in the art will knowof additional techniques that can be used to detect or determine a levelof methylation in the genomic regulatory sequences of the biomarkersdescribed herein.

E. Protein Expression Profiling

A variety of techniques can be used to detect the presence or level ofan expressed protein for determining a protein expression profileaccording to the methods of the present invention. For example, aproteinaceous biomarker can be analyzed using an immunoassay. A proteinexpression profile can also be evaluated using electrophoresis, e.g.,Western blotting, as well as any other technique known to those skilledin the art. Immunoassay techniques and protocols are generally describedin Price and Newman, “Principles and Practice of Immunoassay,” 2ndEdition, Grove's Dictionaries (1997); and Gosling, “Immunoassays: APractical Approach,” Oxford University Press (2000). The presence oramount of the proteinaceous biomarker is typically determined usingantibodies specific for the biomarker and detecting specific binding.For example, a monoclonal antibody directed to EGFR can be obtained fromZymed Laboratories (San Francisco, Calif.) and a monoclonal antibodydirected to TGF-α can be obtained from Oncogene Science (Manhasset,N.Y.). Antibodies directed to other antigens of interest such asreceptor tyrosine kinases (e.g., EGFR, HER2, ErbB3, ErbB4, c-KIT, PDGFA,PDGFB, FLT-3/FLK-2, FLK-1, FLT-1, FLT-4, ROS, ALK, LTK, RET, etc.),tumor suppressors (e.g., PTEN, DMBT1, LGI1, p53, etc.), and growthfactors (e.g., TGF-α, EGF, HB-EGF, VEGF, PDGF, FGF, etc.) can beobtained from Santa Cruz Biotechnology (Santa Cruz, Calif.).

Any suitable immunoassay can be utilized for determining the presence oflevel of one or more proteinaceous biomarkers in a sample. A variety ofimmunoassay techniques, including competitive and non-competitiveimmunoassays, can be used (see, e.g., Self et al., Curr. Opin.Biotechnol., 7:60-65 (1996)). The term immunoassay encompassestechniques including, without limitation, enzyme immunoassays (EIA) suchas enzyme multiplied immunoassay technique (EMIT), enzyme-linkedimmunosorbent assay (ELISA), IgM antibody capture ELISA (MAC ELISA), andmicroparticle enzyme immunoassay (MEIA); capillary electrophoresisimmunoassays (CEIA); radioimmunoassays (RIA); immunoradiometric assays(IRMA); fluorescence polarization immunoassays (FPIA); andchemiluminescence assays (CL). If desired, such immunoassays can beautomated. Preferably, the expression level of proteins such as EGFR andTGF-α are determined using an enzyme immunoassay such as ELISA. Forexample, TGF-α concentration in serum or plasma can be measured using anELISA kit available from R&D Systems (Minneapolis, Minn.), and EGFRlevels can be determined using an ELISA kit from Biosource International(Camarillo, Calif.) or Calbiochem (San Diego, Calif.).

Immunoassays can also be used in conjunction with laser inducedfluorescence (see, e.g., Schmalzing et al., Electrophoresis, 18:2184-93(1997); Bao, J. Chromatogr. B. Biomed. Sci., 699:463-80 (1997)).Liposome immunoassays, such as flow-injection liposome immunoassays andliposome immunosensors, are also suitable for use in the presentinvention (see, e.g., Rongen et al., J. Immunol. Methods, 204:105-133(1997)). In addition, nephelometry assays, in which the formation ofprotein/antibody complexes results in increased light scatter that isconverted to a peak rate signal as a function of the markerconcentration, are suitable for use in the methods of the presentinvention. Nephelometry assays are commercially available from BeckmanCoulter (Brea, Calif.; Kit #449430) and can be performed using a BehringNephelometer Analyzer (Fink et al., J. Clin. Chem. Clin. Biochem.,27:261-276 (1989)).

Specific immunological binding of the antibody to the proteinaceousbiomarker can be detected directly or indirectly. Direct labels includefluorescent or luminescent tags, metals, dyes, radionuclides, and thelike, attached to the antibody. An antibody labeled with iodine-125(¹²⁵I) can be used for determining the level of one or more biomarkersin a sample. A chemiluminescence assay using a chemiluminescent antibodyspecific for the biomarker is suitable for sensitive, non-radioactivedetection of biomarker levels. An antibody labeled with fluorochrome isalso suitable for determining the level of one or more biomarkers in asample. Examples of fluorochromes include, without limitation, DAPI,fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin,R-phycoerythrin, rhodamine, Texas red, and lissamine. Indirect labelsinclude various enzymes well known in the art, such as horseradishperoxidase (HRP), alkaline phosphatase (AP), β-galactosidase, urease,and the like. A horseradish-peroxidase detection system can be used, forexample, with the chromogenic substrate tetramethylbenzidine (TMB),which yields a soluble product in the presence of hydrogen peroxide thatis detectable at 450 nm. An alkaline phosphatase detection system can beused with the chromogenic substrate p-nitrophenyl phosphate, forexample, which yields a soluble product readily detectable at 405 nm.Similarly, a β-galactosidase detection system can be used with thechromogenic substrate o-nitrophenyl-β-D-galactopyranoside (ONPG), whichyields a soluble product detectable at 410 nm. An urease detectionsystem can be used with a substrate such as urea-bromocresol purple(Sigma Immunochemicals; St. Louis, Mo.).

A signal from the direct or indirect label can be analyzed, for example,using a spectrophotometer to detect color from a chromogenic substrate;a radiation counter to detect radiation such as a gamma counter fordetection of ¹²⁵I; or a fluorometer to detect fluorescence in thepresence of light of a certain wavelength. For detection ofenzyme-linked antibodies, a quantitative analysis of the amount ofmarker levels can be made using a spectrophotometer such as an EMAXMicroplate Reader (Molecular Devices; Menlo Park, Calif.) in accordancewith the manufacturer's instructions. If desired, the assays of thepresent invention can be automated or performed robotically, and thesignal from multiple samples can be detected simultaneously.

Antigen capture assays can be useful in the methods of the presentinvention. For example, in an antigen capture assay, an antibodydirected to a proteinaceous biomarker of interest is bound to a solidphase and sample is added such that the biomarker is bound by theantibody. After unbound proteins are removed by washing, the amount ofbound marker can be quantitated using, for example, a radioimmunoassay(see, e.g., Harlow and Lane, Antibodies: A Laboratory Manual, ColdSpring Harbor Laboratory, New York (1988)). Sandwich enzyme immunoassayscan also be useful in the methods of the present invention. For example,in a two-antibody sandwich assay, a first antibody is bound to a solidsupport, and the biomarker is allowed to bind to the first antibody. Theamount of the biomarker is quantitated by measuring the amount of asecond antibody that binds the biomarker. The antibodies can beimmobilized onto a variety of solid supports, such as magnetic orchromatographic matrix particles, the surface of an assay plate (e.g.,microtiter wells), pieces of a solid substrate material or membrane(e.g., plastic, nylon, paper), and the like. An assay strip can beprepared by coating the antibody or a plurality of antibodies in anarray on a solid support. This strip can then be dipped into the testsample and processed quickly through washes and detection steps togenerate a measurable signal, such as a colored spot.

Quantitative Western blotting also can be used to detect or determinethe level of one or more proteinaceous biomarkers in a sample. Westernblots can be quantitated by well-known methods such as scanningdensitometry or phosphorimaging. In certain instances, autoradiographsof the blots are analyzed using a scanning densitometer (MolecularDynamics; Sunnyvale, Calif.) and normalized to a positive control.Values are reported, for example, as a ratio between the actual value tothe positive control (densitometric index). Such methods are well knownin the art as described, e.g., in Parra et al., J. Vasc. Surg.,28:669-675 (1998).

Alternatively, a variety of immunohistochemistry (IHC) techniques can beused to determine the level of one or more proteinaceous biomarkers in asample. As used herein, the term “immunohistochemistry” or “IHC”encompasses techniques that utilize the visual detection of fluorescentdyes or enzymes coupled (i.e., conjugated) to antibodies that react withthe biomarker using fluorescent microscopy or light microscopy andincludes, without limitation, direct fluorescent antibody, indirectfluorescent antibody (IFA), anticomplement immunofluorescence,avidin-biotin immunofluorescence, and immunoperoxidase assays. An IFAassay, for example, is useful for determining whether a sample ispositive for a particular marker of interest, the level of that marker,and/or the staining pattern of that marker. The concentration of themarker in a sample can be quantitated, e.g., through endpoint titrationor through measuring the visual intensity of fluorescence compared to aknown reference standard.

Any IHC technique known to one of skill in the art is suitable for usein the assay methods of the present invention. As a non-limitingexample, IHC can be performed according to the following protocol: (1)slides containing the sample (e.g., tumor tissue) are deparaffinizedwith xylene/70% ethanol into phosphate buffered saline (PBS) at pH 7.4;(2) the slides are then immersed in 10 mM citric acid at pH 6.0,microwaved for about 37 minutes, and cooled down at room temperature(RT) for about 30-60 minutes; (3) endogenous peroxidases are quenchedfor about 10 minutes in 1 part 30% H₂O₂ and 9 parts methanol and theslides are washed 3 times for 3 minutes in PBS; (4) the slides areblocked with blocking reagent at RT for about 30 minutes; (5) antibodiesagainst the biomarker of interest are added and the slides are incubatedat 4° C. overnight; (6) the slides are washed in PBS at RT for about 30minutes, changing the wash buffer every 5 minutes; (7) secondaryantibodies such as biotinylated antibodies are added and the slides areincubated at RT for about 60 minutes; (8) the slides are washed in PBSat RT for about 30 minutes, changing the wash buffer every 5 minutes;(9) streptavidin is added and the slides are incubated at RT for about30 minutes; (10) 3,3′-diaminobenzidine (DAB) is added, the slides areincubated for 5 minutes, the DAB is neutralized with bleach, and theslides are washed for 5 minutes with water; (11) the slides arecounterstained with methylgreen for 3 minutes and washed 3 times withwater; (12) the slides are dipped in 95% ethanol, followed by a 100%ethanol and xylene series; and (13) a coverslip is placed onto theslide.

Examples of IHC protocols for determining the presence or level ofspecific antigens of interest are known in the art. These include theIHC protocols described in, e.g., Ishikawa et al., Cancer Res.,65:9176-9184 (2005) for TGF-α and amphiregulin; Cappuzzo et al., J.Clin. Oncol., 23:5007-5018 (2005) for HER2; Cappuzzo et al., J. Natl.Caner Inst., 97:643-655 (2005) for EGFR; Abrams et al., Mol. Cancer.Ther., 2:471-478 (2003) for c-KIT and PDGFRB; and Lee et al., Anal.Quant. Cytol. Histol., 27:202-210 (2005) for PTEN. Tissue staining canbe visualized using peroxidase-based immunostaining kits available fromVector Laboratories (Burlingame, Calif.) and DAKO (Glostrup, Denmark).

The presence or level of a proteinaceous biomarker can also bedetermined by detecting or quantifying the amount of the purifiedmarker. Purification of the marker can be achieved, for example, by highpressure liquid chromatography (HPLC), alone or in combination with massspectrometry (e.g., MALDI/MS, MALDI-TOF/MS, tandem MS, etc.).Qualitative or quantitative detection of a biomarker can also bedetermined by well-known methods including, without limitation, Bradfordassays, Coomassie blue staining, silver staining, assays forradiolabeled protein, and mass spectrometry.

The analysis of a plurality of proteinaceous biomarkers may be carriedout separately or simultaneously with one test sample. For separate orsequential assay of biomarkers, suitable apparatuses include clinicallaboratory analyzers such as the ElecSys (Roche), the AxSym (Abbott),the Access (Beckman), the ADVIA®, the CENTAUR® (Bayer), and the NICHOLSADVANTAGE® (Nichols Institute) immunoassay systems. Preferredapparatuses or protein chips perform simultaneous assays of a pluralityof biomarkers on a single surface. Particularly useful physical formatscomprise surfaces having a plurality of discrete, addressable locationsfor the detection of a plurality of different biomarkers. Such formatsinclude protein microarrays, or “protein chips” (see, e.g., Ng et al.,J. Cell Mol. Med., 6:329-340 (2002)) and certain capillary devices (see,e.g., U.S. Pat. No. 6,019,944). In these embodiments, each discretesurface location may comprise antibodies to immobilize one or morebiomarkers for detection at each location. Surfaces may alternativelycomprise one or more discrete particles (e.g., microparticles ornanoparticles) immobilized at discrete locations of a surface, where themicroparticles comprise antibodies to immobilize one or more biomarkersfor detection.

In view of the above, one skilled in the art will readily appreciatethat the methods of the present invention for determining a proteinexpression profile from a sample of a subject can be practiced using oneor any combination of the well-known techniques described above or othertechniques known in the art.

F. Protein Activation Profiling

Analysis of the activation or inhibition of proteinaceous biomarkers ofinterest can be used alone or in combination with other markers topredict, monitor, or optimize tyrosine kinase inhibitor therapy in asubject according to the methods of the present invention. Any methodknown in the art for detecting or determining the activity or activationstate of one or more of the biomarkers described herein is suitable foruse in the present invention.

In some embodiments, the activation or inhibition of a proteinaceousbiomarker can be determined by molecular cytogenetic techniques such asimmunohistochemistry (IHC). An IHC assay is particularly useful fordetermining the phosphorylation state of proteins that are activated orinhibited by phosphorylation at specific tyrosine, serine, and/orthreonine residues. In particular, IHC can be performed to determinewhether a sample is positive for a particular phosphorylated marker ofinterest, the level of that phosphorylated marker, and/or the stainingpattern of that phosphorylated marker. As a non-limiting example,paraffin-embedded tumor tissue sections can be stained with antibodiesagainst phospho-Akt (P-Akt) and phospho-MAPK (P-MAPK) as described inCappuzzo et al., J. Natl. Caner Inst., 96:1133-1141 (2004) to determinetheir activation state. Negative to weak P-MAPK staining typicallyindicates the absence of active MAPK, whereas moderate to strongstaining generally indicates the presence of active MAPK. Sinceactivation of Akt by phosphorylation results in the translocation ofP-Akt from the cytoplasm to the nucleus, the presence of P-Akt stainingin the nucleus indicates the presence of active Akt, whereas the absenceof nuclear staining indicates the absence of active Akt. An additionalIHC protocol for detecting P-Akt in tumor cells is described in Cappuzzoet al., J. Natl. Caner Inst., 97:643-655 (2005).

Phospho-specific antibodies against various phosphorylated forms ofproteins such as Akt, MAPK, EGFR, c-KIT, c-Src, FLK-1, PDGFRA, PDGFRB,PTEN, Raf, and MEK are available from Santa Cruz Biotechnology (SantaCruz, Calif.). Such phospho-specific antibodies can also be used intechniques including any of the immunoassays described above (e.g.,ELISA) as well as Western blotting and immunoprecipitation assays todetermine the activation state of a protein of interest according to themethods of the present invention. For example, specific tyrosinephosphorylated forms of EGFR can be detected using EGFR Phospho ELISAkits available from Sigma-Aldrich (St. Louis, Mo.).

Other methods for detecting the phosphorylation state of a protein ofinterest include, but are not limited to, KIRA ELISA (see, e.g., U.S.Pat. Nos. 5,766,863; 5,891,650; 5,914,237; 6,025,145; and 6,287,784),mass spectrometry (comparing size of phosphorylated and unphosphorylatedprotein), and the eTag™ assay system.

When at least one of the proteinaceous biomarkers of interest is anenzyme, a level of enzymatic activity can be determined to assess theactivation state of the enzyme. For example, any of the receptor ornon-receptor tyrosine kinases described herein can be assayed for thepresence or level of kinase activity using an appropriate substrate.Similarly, any tyrosine or serine/threonine kinase or phosphataseinvolved in the downstream signaling of receptor tyrosine kinases can beassayed for the presence or level of kinase activity using a suitablesubstrate. Tyrosine kinase activity can be determined using kitsavailable from Chemicon International, Inc. (Temecula, Calif.) andQIAGEN Inc. (Valencia, Calif.). A fluorescent-based tyrosine kinase ortyrosine phosphatase activity assay is available from PromegaCorporation (Madison, Wis.) and is described in Goueli et al., CellNotes, 8:15-20 (2004). The activity of serine/threonine kinases such asAkt and MAPK can be determined using a kit available from StressgenBioreagents (Victoria, BC, Canada) and Chemicon International, Inc.,respectively.

In some embodiments, the activation state of interest corresponds to thephosphorylation state of a proteinaceous biomarker, the ubiquitinationstate of the biomarker, or the complexation state of the biomarker withanother cellular molecule. Non-limiting examples of activation states(listed in parentheses) of tyrosine kinases and their signalingcomponents that are suitable for detection include: EGFR (EGFRvIII,phosphorylated (p-) EGFR, EGFR:Shc, ubiquitinated (u-) EGFR,p-EGFRvIII); ErbB2 (p85:truncated (Tr)-ErbB2, p-ErbB2, p85:Tr-p-ErbB2,Her2:Shc, ErbB2:PI3K, ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4); ErbB3(p-ErbB3, ErbB3:PI3K, p-ErbB3:PI3K, ErbB3:Shc); ErbB4 (p-ErbB4,ErbB4:Shc); IGF-1R (p-IGF-1R, IGF-1R:IRS, IRS:PI3K, p-IRS, IGF-1R:PI3K);INSR (p-INSR); KIT (p-KIT); FLT3 (p-FLT3); HGFRI (p-HGFRI); HGFR2(p-HGFR2); RET (p-RET); PDGFRa (p-PDGFRa); PDGFRP (p-PDGFRP); VEGFRI(p-VEGFRI, VEGFRI:PLCg, VEGFRI:Src); VEGFR2 (p-VEGFR2, VEGFR2:PLCy,VEGFR2:Src, VEGFR2:heparin sulphate, VEGFR2:VE-cadherin); VEGFR3(p-VEGFR3); FGFR1 (p-FGFR1); FGFR2 (p-FGFR2); FGFR3 (p-FGFR3); FGFR4(p-FGFR4); Tie1 (p-Tie1); Tie2 (p-Tie2); EphA (p-EphA); EphB (p-EphB);NFKB and/or IKB (p-IK (S32), p-NFKB (5536), p-P65:IKBa); Akt (p-Akt(T308, 5473)); PTEN (p-PTEN); Bad (p-Bad (5112, S136), Bad: 14-3-3);mTor (p-mTor (52448)); p70S6K (p-p70S6K (T229, T389)); Mek (p-Mek (5217,5221)); Erk (p-Erk (T202, Y204)); Rsk-1 (p-Rsk-1 (T357, 5363)); Jnk(p-Jnk (T183, Y185)); P38 (p-P38 (T180, Y182)); Stat3 (p-Stat-3 (Y705,5727)); Fak (p-Fak (Y576)); Rb (p-Rb (5249, T252, 5780)); Ki67; p53(p-p53 (5392, S20)); CREB (p-CREB (5133)); c-Jun (p-c-Jun (S63)); cSrc(p-cSrc (Y416)); and paxillin (p-paxillin (Y118)).

Any method known in the art can be used to detect the complexation stateof a proteinaceous biomarker of interest with another cellular molecule.Preferably, the formation of heterodimeric complexes between members ofthe EGFR family of receptor tyrosine kinases are detected in tumors ortumor cells. Several preferred methods are described below. Thesemethods generally detect noncovalent protein-protein interactionsbetween proteins of interest.

Immunoaffinity-based methods, such as immunoprecipitation or ELISA, canbe used to detect heterodimeric complexes between proteins of interest(e.g., EGFR heterodimers). In one embodiment, antibodies against aparticular EGFR subtype are used to immunoprecipitate complexescomprising that EGFR subtype from tumor cells, and the resultingimmunoprecipitant is then probed for the presence of one or moreadditional EGFR subtypes by immunoblotting. In another embodiment, EGFRligands specific to one or more types of EGFR heterodimers can be usedto precipitate complexes, which are then probed for the presence of eachEGFR subtype present in the complexes. In certain instances, the EGFRligands can be conjugated to avidin and EGFR heterodimeric complexespurified on a biotin column.

In other embodiments, such as ELISA or antibody sandwich-type assays,antibodies against a particular EGFR subtype are immobilized on a solidsupport, contacted with tumor cells or tumor cell lysate, washed, andthen exposed to antibodies against one or more additional EGFR subtypes.Binding of the latter antibody, which may be detected directly or by asecondary antibody conjugated to a detectable label, indicates thepresence of EGFR heterodimers. In certain instances, EGFR ligands may beused in place of, or in combination with, antibodies against EGFRsubtypes.

Immunoprecipitation with antibodies against EGFR subtypes can befollowed by a functional assay for heterodimers, as an alternative orsupplement to immunoblotting. In one embodiment, immunoprecipitationwith antibodies against a particular EGFR subtype is followed by anassay for receptor tyrosine kinase activity in the immunoprecipitant. Asa non-limiting example involving the detection of ErbB2:ErbB3heterodimers, the presence of tyrosine kinase activity in theimmunoprecipitant indicates that ErbB3 is most likely associated withErbB2 because ErbB3 does not have intrinsic tyrosine kinase activity(see, e.g., Graus-Porta et al., EMBO J., 16:1647-1655 (1997); Klapper etal., Proc. Natl. Acad. Sci. USA, 96:4995-5000 (1999)). As anothernon-limiting example involving the detection of ErbB2:EGFR heterodimers,immunoprecipitation with ErbB2 antibody can be followed by an assay forEGFR kinase activity. In this assay, the immunoprecipitant can becontacted with radioactive ATP and a peptide substrate that mimics thein vivo site of transphosphorylation of ErbB2 by EGFR. Phosphorylationof the peptide indicates co-immunoprecipitation and thusheterodimerization of EGFR with ErbB2. Receptor tyrosine kinase activityassays are well known in the art and include assays that detectphosphorylation of target substrates, for example, by phosphotyrosineantibody, and activation of cognate signal transduction pathways, suchas the MAPK pathway.

Chemical or UV cross-linking can also be used to covalently joinheterodimers on the surface of living tumor cells (see, e.g., Hunter etal., Biochem. Jr., 320:847-853 (1996)). Examples of chemicalcross-linkers include, but are not limited to, dithiobis(succinimidyl)propionate (DSP) and 3,3′-dithiobis(sulphosuccinim-idyl) propionate (DTSSP). In one embodiment, cell extracts from chemically cross-linked tumorcells are analyzed by SDS-PAGE and immunoblotted with antibodies to oneor more antibodies against EGFR subtypes. A supershifted band of theappropriate molecular weight most likely represents specific EGFRheterodimers. This result may be confirmed by subsequent immunoblottingwith the appropriate antibodies.

Fluorescence resonance energy transfer (FRET) can also be used to detectheterodimers between members of the EGFR family of receptor tyrosinekinases. FRET detects protein conformational changes and protein-proteininteractions in vivo and in vitro based on the transfer of energy from adonor fluorophore to an acceptor fluorophore (see, e.g., Selvin, Nat.Struct. Biol., 7:730-734 (2000)). Energy transfer takes place only ifthe donor fluorophore is in sufficient proximity to the acceptorfluorophore. In a typical FRET experiment, two proteins or two sites ona single protein are labeled with different fluorescent probes. One ofthe probes, the donor probe, is excited to a higher energy state byincident light of a specified wavelength. The donor probe then transmitsits energy to the second probe, the acceptor probe, resulting in areduction in the donor's fluorescence intensity and an increase in theacceptor's fluorescence emission. To measure the extent of energytransfer, the donor's intensity in a sample labeled with donor andacceptor probes is compared with its intensity in a sample labeled withdonor probe only. Optionally, acceptor intensity is compared indonor/acceptor and acceptor only samples. Suitable probes are known inthe art and include, for example, membrane permeant dyes (e.g.,fluorescein, rhodamine, etc.), organic dyes (e.g., cyanine dyes, etc.),and lanthanide atoms. Methods and instrumentation for detecting andmeasuring energy transfer are known in the art.

FRET-based techniques suitable for detecting and measuringprotein-protein interactions in individual cells are also known in theart. For example, donor photobleaching fluorescence resonance energytransfer (pbFRET) microscopy and fluorescence lifetime imagingmicroscopy (FLIM) may be used to detect the dimerization of cell surfacereceptors (see, e.g., Selvin, supra; Gadella et al., J. Cell Biol.,129:1543-1558 (1995)). In one embodiment, pbFRET is used on cells either“in suspension” or “in situ” to detect and measure the formation of EGFRheterodimers, as described, e.g., in Nagy et al., Cytometry, 32:120-131(1998). These techniques measure the reduction in a donor's fluorescencelifetime due to energy transfer. In a particular embodiment, a flowcytometric Foerster-type FRET technique (FCET) may be used toinvestigate EGFR heterodimerization, as described, e.g., in Nagy et al.,supra, and Brockhoff et al., Cytometry, 44:33848 (2001).

FRET is preferably used in conjunction with standard immunohistochemicallabeling techniques (see, e.g., Kenworthy, Methods, 24:289-296 (2001).For example, antibodies conjugated to suitable fluorescent dyes can beused as probes for labeling two different proteins. If the proteins arewithin proximity of one another, the fluorescent dyes act as donors andacceptors for FRET. Energy transfer can be detected by standard means.Energy transfer may be detected by flow cytometric means or by digitalmicroscopy systems, such as confocal microscopy or wide-fieldfluorescence microscopy coupled to a charge-coupled device (CCD) camera.

In one embodiment of the present invention, antibodies against differentEGFR subtypes are directly labeled with two different fluorophores.Tumor cells or tumor cell lysates are contacted with the differentiallylabeled antibodies, which act as donors and acceptors for FRET in thepresence of particular EGFR heterodimers. Alternatively, unlabeledantibodies against the different EGFR subtypes are used along withdifferentially labeled secondary antibodies that serve as donors andacceptors. Energy transfer can be detected and the presence of EGFRheterodimers determined if the labels are found to be in closeproximity.

In another embodiment, the presence of EGFR heterodimers on the surfaceof tumor cells is demonstrated by co-localization of EGFR subtypes usingstandard direct or indirect immunofluorescence techniques and confocallaser scanning microscopy. Alternatively, laser scanning imaging (LSI)can be used to detect antibody binding and co-localization of EGFRsubtypes in a high-throughput format, such as a microwell plate, asdescribed, e.g., in Zuck et al., Proc. Natl. Acad. Sci. USA,96:11122-11127 (1999).

In further embodiments, the presence of EGFR heterodimers is determinedby identifying enzymatic activity that is dependent upon the proximityof the heterodimer components. Antibodies against an EGFR subtype areconjugated with one enzyme and antibodies against another EGFR subtypeare conjugated with a second enzyme. A first substrate for the firstenzyme is added and the reaction produces a second substrate for thesecond enzyme. This leads to a reaction with another molecule to producea detectable compound, such as a dye. The presence of another chemicalbreaks down the second substrate, so that reaction with the secondenzyme is prevented unless the first and second enzymes, and thus thetwo antibodies, are in close proximity. In a particular embodiment,tumor cells or tumor cell lysates are contacted with an ErbB2 antibodythat is conjugated with glucose oxidase and an ErbB3 or EGFR antibodythat is conjugated with horseradish peroxidase. Glucose is added to thereaction, along with a dye precursor, such as DAB, and catalase. Thepresence of EGFR heterodimers is determined by the development of colorupon staining for DAB.

Heterodimers may also be detected using methods based on the eTag™ assaysystem as described, e.g., in U.S. Pat. No. 6,673,550. An eTag™, or“electrophoretic tag,” comprises a detectable reporter moiety, such as afluorescent group. It may also comprise a “mobility modifier,” whichcomprises a moiety having a unique electrophoretic mobility. Thesemoieties allow for separation and detection of the eTag™ from a complexmixture under defined electrophoretic conditions, such as capillaryelectrophoresis (CE). The portion of the eTag™ containing the reportermoiety and, optionally, the mobility modifier is linked to a firsttarget binding moiety by a cleavable linking group to produce a firstbinding compound. The first target binding moiety specificallyrecognizes a particular first target, such as a nucleic acid or protein.The first target binding moiety is not limited in any way, and may be,for example, a polynucleotide or a polypeptide. Preferably, the firsttarget binding moiety is an antibody or antibody fragment.Alternatively, the first target binding moiety may be an EGFR ligand orbinding-competent fragment thereof.

The linking group preferably comprises a cleavable moiety, such as anenzyme substrate, or any chemical bond that may be cleaved under definedconditions. When the first target binding moiety binds to its target,the cleaving agent is introduced and/or activated, and the linking groupis cleaved, thus releasing the portion of the eTag™ containing thereporter moiety and mobility modifier. Thus, the presence of a “free”eTag™ indicates the binding of the target binding moiety to its target.

Preferably, a second binding compound comprises the cleaving agent and asecond target binding moiety that specifically recognizes a secondtarget. The second target binding moiety is also not limited in any wayand may be, for example, an antibody or antibody fragment or an EGFRligand or binding-competent fragment thereof. The cleaving agent is suchthat it will only cleave the linking group in the first binding compoundIf the first binding compound and the second binding compound are inclose proximity.

As a non-limiting example, a first binding compound comprises an eTag™in which antibodies against ErbB2 serve as the first target bindingmoiety. A second binding compound comprises antibodies against EGFR orErbB3 joined to a cleaving agent capable of cleaving the linking groupof the eTag™. Preferably, the cleaving agent must be activated in orderto be able to cleave the linking group. Tumor cells or tumor celllysates are contacted with the eTag™, which binds to ErbB2, and with themodified EGFR or ErbB3 antibodies, which binds to EGFR or ErbB3 on thecell surface. Unbound binding compound is preferably removed, and thecleaving agent is activated, if necessary. If EGFR:ErbB2 or ErbB2:ErbB3heterodimers are present, the cleaving agent will cleave the linkinggroup and release the eTag™ due to the proximity of the cleaving agentto the linking group. Free eTag™ may then be detected by any methodknown in the art, such as capillary electrophoresis. In certaininstances, the cleaving agent is an activatable chemical species thatacts on the linking group. For example, the cleaving agent may beactivated by exposing the sample to light.

In view of the above, one skilled in the art will readily appreciatethat the methods of the present invention for determining a proteinactivation profile from a sample of a subject can be practiced using oneor any combination of the well-known techniques described above or othertechniques known in the art.

VI. Selection of Antibodies

The generation and selection of antibodies not already commerciallyavailable for detecting or determining the level of proteinaceousbiomarkers may be accomplished several ways. For example, one way is topurify polypeptides of interest or to synthesize the polypeptides ofinterest using, e.g., solid phase peptide synthesis methods well knownin the art. See, e.g., Guide to Protein Purification, Murray P.Deutcher, ed., Meth. Enzymol., Vol. 182, 1990; Solid Phase PeptideSynthesis, Greg B. Fields, ed., Meth. Enzymol., Vol. 289, 1997; Kiso etal., Chem. Pharm. Bull., 38:1192-99 (1990); Mostafavi et al., Biomed.Pept. Proteins Nucleic Acids, 1:255-60, (1995); Fujiwara et al., Chem.Pharm. Bull., 44:1326-31 (1996). The selected polypeptides may then beinjected, for example, into mice or rabbits, to generate polyclonal ormonoclonal antibodies. One skilled in the art will recognize that manyprocedures are available for the production of antibodies, for example,as described in Antibodies, A Laboratory Manual, Harlow and Lane, Eds.,Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1988). Oneskilled in the art will also appreciate that binding fragments or Fabfragments which mimic antibodies can also be prepared from geneticinformation by various procedures (see, e.g., Antibody Engineering: APractical Approach, Borrebaeck, Ed., Oxford University Press, Oxford(1995); J. Immunol., 149:3914-3920 (1992)).

In addition, numerous publications have reported the use of phagedisplay technology to produce and screen libraries of polypeptides forbinding to a selected target (see, e.g, Cwirla et al., Proc. Natl. Acad.Sci. USA, 87:6378-6382 (1990); Devlin et al., Science, 249:404-406(1990); Scott et al., Science, 249:386-388 (1990); and Ladner et al.,U.S. Pat. No. 5,571,698). A basic concept of phage display methods isthe establishment of a physical association between DNA encoding apolypeptide to be screened and the polypeptide. This physicalassociation is provided by the phage particle, which displays apolypeptide as part of a capsid enclosing the phage genome which encodesthe polypeptide. The establishment of a physical association betweenpolypeptides and their genetic material allows simultaneous massscreening of very large numbers of phage bearing different polypeptides.Phage displaying a polypeptide with affinity to a target bind to thetarget and these phage are enriched by affinity screening to the target.The identity of polypeptides displayed from these phage can bedetermined from their respective genomes. Using these methods apolypeptide identified as having a binding affinity for a desired targetcan then be synthesized in bulk by conventional means (see, e.g., U.S.Pat. No. 6,057,098).

The antibodies that are generated by these methods may then be selectedby first screening for affinity and specificity with the purifiedpolypeptide of interest and, if required, comparing the results to theaffinity and specificity of the antibodies with polypeptides that aredesired to be excluded from binding. The screening procedure can involveimmobilization of the purified polypeptides in separate wells ofmicrotiter plates. The solution containing a potential antibody or groupof antibodies is then placed into the respective microtiter wells andincubated for about 30 min to 2 h. The microtiter wells are then washedand a labeled secondary antibody (e.g., an anti-mouse antibodyconjugated to alkaline phosphatase if the raised antibodies are mouseantibodies) is added to the wells and incubated for about 30 min andthen washed. Substrate is added to the wells and a color reaction willappear where antibody to the immobilized polypeptide(s) are present.

The antibodies so identified may then be further analyzed for affinityand specificity in the assay design selected. In the development ofimmunoassays for a target protein, the purified target protein acts as astandard with which to judge the sensitivity and specificity of theimmunoassay using the antibodies that have been selected. Because thebinding affinity of various antibodies may differ, certain antibodypairs (e.g., in sandwich assays) may interfere with one anothersterically, etc., assay performance of an antibody may be a moreimportant measure than absolute affinity and specificity of an antibody.

Those skilled in the art will recognize that many approaches can betaken in producing antibodies or binding fragments and screening andselecting for affinity and specificity for the various polypeptides, butthese approaches do not change the scope of the present invention.

VII. Algorithms

The present invention provides assay methods for predicting, monitoring,or optimizing tyrosine kinase inhibitor therapy in a subject using analgorithmic analysis of a panel of biomarkers in a sample from thesubject. In particular, the algorithms described herein canadvantageously provide improved sensitivity, specificity, negativepredictive value, positive predictive value, and/or overall accuracy forcarrying out the methods of the present invention.

The term “algorithm” includes any of a variety of statistical analysesused to determine relationships between variables. In some embodimentsof the present invention, the variables are profiles such as nucleicacid and/or protein profiles. In these embodiments, the algorithm isused, e.g., to predict, identify, monitor, and/or optimize tyrosinekinase inhibitor efficacy, toxicity, and/or resistance in a tumor, tumorcell, or patient. Any number of profiles can be analyzed using analgorithm according to the methods of the present invention. Forexample, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 25, 30, 35, 40, 45, 50, or more profiles can be included in analgorithm. In one embodiment, logistic regression is used. In anotherembodiment, linear regression is used. In certain instances, thealgorithms of the present invention can use a quantile measurement of aparticular profile within a given population as a variable. Quantilesare a set of “cut points” that divide a sample of data into groupscontaining (as far as possible) equal numbers of observations. Forexample, quartiles are values that divide a sample of data into fourgroups containing (as far as possible) equal numbers of observations.The lower quartile is the data value a quarter way up through theordered data set; the upper quartile is the data value a quarter waydown through the ordered data set. Quintiles are values that divide asample of data into five groups containing (as far as possible) equalnumbers of observations. The present invention can also include the useof percentile ranges of profiles (e.g., tertiles, quartile, quintiles,etc.), or their cumulative indices (e.g., quartile sums of profiles,etc.) as variables in the algorithms (just as with continuousvariables).

As used herein, the term “index value” refers to a number for a subjectthat is determined using an algorithm according to the methods of thepresent invention. For example, the index value may be determined usinglogistic regression and correspond to a number between 0 and 1, e.g.,0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and further divisionsthereof such as 0.25 or 0.225. Preferably, the index value is presentedas a “cumulative index value,” which represents a summation of thosevalues determined from the assessment of at least one nucleic acidand/or protein profile (see, e.g., Examples 1 and 2 below). Thecumulative index value can be compared to an index cut-off value, or theratio of cumulative index values of all tested profiles can be dividedby an index cut-off value, e.g., to predict, identify, monitor, and/oroptimize tyrosine kinase inhibitor efficacy, toxicity, and/or resistancein a tumor, tumor cell, or patient.

The term “index cutoff value” refers to a number chosen on the basis ofpopulation analysis that is used for comparison to an index valuecalculated for a subject. Thus, the index cutoff value is based onanalysis of index values determined using an algorithm. Those of skillin the art will recognize that an index cutoff value can be determinedaccording to the needs of the user and characteristics of the analyzedpopulation. When the algorithm is logistic regression, the index cutoffvalue will, of necessity, be between 0 and 1, e.g., between 0.1 to 0.9,0.2 to 0.8, 0.3 to 0.7, or 0.4 to 0.6. Preferably, the index cutoffvalue is calculated according to the formulas set forth in Examples 1and 2 below.

The term “iterative approach” refers to the analysis of at least oneprofile associated with cancer from a subject using more than onealgorithm and/or index cutoff value. For example, two or more algorithmscould be used to analyze different sets of profiles. As another example,a single algorithm could be used to analyze at least one profile, butmore than one index cutoff value based on the algorithm could be used inthe methods of the present invention.

In certain instances, cut-off values can be determined and independentlyadjusted for each of a number of biomarkers to observe the effects ofthe adjustments on clinical parameters such as sensitivity, specificity,negative predictive value, positive predictive value, and overallaccuracy. In particular, Design of Experiments (DOE) methodology can beused to simultaneously vary the cut-off values and to determine theeffects on the resulting clinical parameters of sensitivity,specificity, negative predictive value, positive predictive value, andoverall accuracy. The DOE methodology is advantageous in that variablesare tested in a nested array requiring fewer runs and cooperativeinteractions among the cut-off variables can be identified. Optimizationsoftware such as DOE Keep It Simple Statistically (KISS) can be obtainedfrom Air Academy Associates (Colorado Springs, Colo.) and can be used toassign experimental runs and perform the simultaneous equationcalculations. Using the DOE KISS program, an optimized set of cut-offvalues for a given clinical parameter and a given set of biomarkers canbe calculated. ECHIP optimization software, available from ECHIP, Inc.(Hockessin, Del.), and Statgraphics optimization software, availablefrom STSC, Inc. (Rockville, Md.), are also useful for determiningcut-off values for a given set of biomarkers. Alternatively, cut-offvalues can be determined using Receiver Operating Characteristic (ROC)curves and adjusted to achieve the desired clinical parameter values.

In some embodiments, the algorithms of the present invention compriseone or more learning statistical classifier systems. As used herein, theterm “learning statistical classifier system” refers to a machinelearning algorithmic technique capable of adapting to complex data setsand making decisions based upon such data sets. In some embodiments, oneor more learning statistical classifier systems are used, e.g., 2, 3, 4,5, 6, 7, 8, 9, 10, or more learning statistical classifier systems areused, preferably in tandem. Examples of learning statistical classifiersystems include, but are not limited to, those using inductive learning(e.g., decision/classification trees such as random forests,classification and regression trees (CART), boosted trees, etc.),Probably Approximately Correct (PAC) learning, connectionist learning(e.g., neural networks (NN), artificial neural networks (ANN), neurofuzzy networks (NFN), network structures, perceptrons such asmulti-layer perceptrons, multi-layer feed-forward networks, applicationsof neural networks, Bayesian learning in belief networks, etc.),reinforcement learning (e.g., passive learning in a known environmentsuch as naïve learning, adaptive dynamic learning, and temporaldifference learning, passive learning in an unknown environment, activelearning in an unknown environment, learning action-value functions,applications of reinforcement learning, etc.), and genetic algorithmsand evolutionary programming. Other learning statistical classifiersystems include support vector machines (e.g., Kernel methods),multivariate adaptive regression splines (MARS), Levenberg-Marquardtalgorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradientdescent algorithms, and learning vector quantization (LVQ).

Random forests are learning statistical classifier systems that areconstructed using an algorithm developed by Leo Breiman and AdeleCutler. Random forests use a large number of individual decision treesand decide the class by choosing the mode (i.e., most frequentlyoccurring) of the classes as determined by the individual trees. Randomforest analysis can be performed, e.g., using the RandomForests softwareavailable from Salford Systems (San Diego, Calif.). See, e.g., Breiman,Machine Learning, 45:5-32 (2001); andhttp://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,for a description of random forests.

Classification and regression trees represent a computer intensivealternative to fitting classical regression models and are typicallyused to determine the best possible model for a categorical orcontinuous response of interest based upon one or more predictors.Classification and regression tree analysis can be performed, e.g.,using the CART software available from Salford Systems or the Statisticadata analysis software available from StatSoft, Inc. (Tulsa, Okla.). Adescription of classification and regression trees is found, e.g., inBreiman et al. “Classification and Regression Trees,” Chapman and Hall,New York (1984); and Steinberg et al., “CART: Tree-StructuredNon-Parametric Data Analysis,” Salford Systems, San Diego, (1995).

Neural networks are interconnected groups of artificial neurons that usea mathematical or computational model for information processing basedon a connectionist approach to computation. Typically, neural networksare adaptive systems that change their structure based on external orinternal information that flows through the network. Specific examplesof neural networks include feed-forward neural networks such asperceptrons, single-layer perceptrons, multi-layer perceptrons,backpropagation networks, ADALINE networks, MADALINE networks,Leammatrix networks, radial basis function (RBF) networks, andself-organizing maps or Kohonen self-organizing networks; recurrentneural networks such as simple recurrent networks and Hopfield networks;stochastic neural networks such as Boltzmann machines; modular neuralnetworks such as committee of machines and associative neural networks;and other types of networks such as instantaneously trained neuralnetworks, spiking neural networks, dynamic neural networks, andcascading neural networks. Neural network analysis can be performed,e.g., using the Statistica data analysis software available fromStatSoft, Inc. See, e.g., Freeman et al., In “Neural Networks:Algorithms, Applications and Programming Techniques,” Addison-WesleyPublishing Company (1991); Zadeh, Information and Control, 8:338-353(1965); Zadeh, “IEEE Trans. on Systems, Man and Cybernetics,” 3:28-44(1973); Gersho et al., In “Vector Quantization and Signal Compression,”Kluywer Academic Publishers, Boston, Dordrecht, London (1992); andHassoun, “Fundamentals of Artificial Neural Networks,” MIT Press,Cambridge, Mass., London (1995), for a description of neural networks.

Support vector machines are a set of related supervised learningtechniques used for classification and regression and are described,e.g., in Cristianini et al., “An Introduction to Support Vector Machinesand Other Kernel-Based Learning Methods,” Cambridge University Press(2000). Support vector machine analysis can be performed, e.g., usingthe SVM^(light) software developed by Thorsten Joachims (CornellUniversity) or using the LIBSVM software developed by Chih-Chung Changand Chih-Jen Lin (National Taiwan University).

The learning statistical classifier systems described herein can betrained and tested using a cohort of samples from healthy individuals,cancer patients, cancer cell lines, and the like. For example, samplesfrom patients diagnosed by a physician, and preferably by an oncologist,as having cancer are suitable for use in training and testing thelearning statistical classifier systems of the present invention.Samples from healthy individuals can include those that were notidentified as having cancer. In certain embodiments, samples from cancercell lines can be used in training and testing the learning statisticalclassifier systems described herein (see, e.g., Example 4 below). Oneskilled in the art will know of additional techniques and diagnosticcriteria for obtaining a cohort of samples that can be used in trainingand testing the learning statistical classifier systems of the presentinvention.

As used herein, the term “sensitivity” refers to the probability that analgorithm of the present invention gives a positive result when thesample is positive. Sensitivity is calculated as the number of truepositive results divided by the sum of the true positives and falsenegatives. Sensitivity essentially is a measure of how well an algorithmof the present invention correctly identifies responders (e.g., subjectslikely to respond to tyrosine kinase inhibitor therapy, subjects withoutacquired resistance to tyrosine kinase inhibitor therapy, etc.) fromnon-responders. The marker values or learning statistical classifiermodels (e.g., random forest or neural network models) can be selectedsuch that the sensitivity is at least about 60%, and can be, forexample, at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%,82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,96%, 97%, 98%, or 99%.

The term “specificity” as used herein refers to the probability that analgorithm of the present invention gives a negative result when thesample is not positive. Specificity is calculated as the number of truenegative results divided by the sum of the true negatives and falsepositives. Specificity essentially is a measure of how well an algorithmof the present invention excludes non-responders from responders. Themarker values or learning statistical classifier models can be selectedsuch that the specificity is at least about 70%, for example, at leastabout 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,96%, 97%, 98%, or 99%.

As used herein, the term “negative predictive value” or “NPV” refers tothe probability that a subject classified as a non-responder is actuallyunlikely to respond to tyrosine kinase inhibitor therapy or hasdeveloped acquired resistance to tyrosine kinase inhibitor therapy.Negative predictive value can be calculated as the number of truenegatives divided by the sum of the true negatives and false negatives.Negative predictive value is determined by the characteristics of thealgorithm as well as the prevalence of the disease in the populationanalyzed. The marker values or learning statistical classifier modelscan be selected such that the negative predictive value in a populationhaving a disease prevalence is at least about 70% and can be, forexample, at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%,84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98%, or 99%.

The term “positive predictive value” or “PPV” as used herein refers tothe probability that an individual classified as a responder is actuallylikely to respond to tyrosine kinase inhibitor therapy or has notdeveloped acquired resistance to tyrosine kinase inhibitor therapy.Positive predictive value can be calculated as the number of truepositives divided by the sum of the true positives and false positives.Positive predictive value is determined by the characteristics of thealgorithm as well as the prevalence of the disease in the populationanalyzed. The marker values or learning statistical classifier modelscan be selected such that the positive predictive value in a populationhaving a disease prevalence is at least about 25% and can be, forexample, at least about 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%.

Predictive values, including negative and positive predictive values,are influenced by the prevalence of the disease in the populationanalyzed. In the algorithms of the present invention, the marker valuesor learning statistical classifier models can be selected to produce adesired clinical parameter for a clinical population with a particularcancer prevalence. For example, marker values or learning statisticalclassifier models can be selected for a cancer prevalence of at leastabout 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, or 70%, which can be seen, e.g., in a clinician's office or ageneral practitioner's office.

As used herein, the term “overall accuracy” refers to the accuracy withwhich an algorithm of the present invention classifies responders andnon-responders. Overall accuracy is calculated as the sum of the truepositives and true negatives divided by the total number of sampleresults and is affected by the prevalence of the disease in thepopulation analyzed. For example, the marker values or learningstatistical classifier models can be selected such that the overallaccuracy in a patient population having a disease prevalence is at leastabout 60%, and can be, for example, at least about 65%, 70%, 75%, 76%,77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,95%, 96%, 97%, 98%, or 99%.

VIII. Methods of Administration

According to the methods of the present invention, the compoundsdescribed herein (e.g., tyrosine kinase inhibitors such as gefitinib andsunitinib) are administered to a subject by any convenient means knownin the art. The assay methods of the present invention can be used tooptimize dosage of tyrosine kinase inhibitors in subjects who have notreceived any tyrosine kinase inhibitor therapy as well as subjects whoare currently undergoing tyrosine kinase inhibitor therapy. The assaymethods of the present invention can also be used to reduce toxicity totyrosine kinase inhibitors in subjects who have not received anytyrosine kinase inhibitor therapy as well as subjects who are currentlyundergoing tyrosine kinase inhibitor therapy. One skilled in the artwill appreciate that tyrosine kinase inhibitors can be administeredalone or as part of a combined therapeutic approach with conventionalchemotherapy, radiotherapy, hormonal therapy, immunotherapy, and/orsurgery.

Tyrosine kinase inhibitors can be administered with a suitablepharmaceutical excipient as necessary and can be carried out via any ofthe accepted modes of administration. Thus, administration can be, forexample, oral, buccal, sublingual, gingival, palatal, intravenous,topical, subcutaneous, transcutaneous, transdermal, intramuscular,intra-joint, parenteral, intra-arteriole, intradermal, intraventricular,intracranial, intraperitoneal, intravesical, intrathecal, intralesional,intranasal, rectal, vaginal, or by inhalation. By “co-administer” it ismeant that a tyrosine kinase inhibitor is administered at the same time,just prior to, or just after the administration of a second drug (e.g.,another tyrosine kinase inhibitor, a drug useful for reducing theside-effects associated with tyrosine kinase inhibitor therapy, achemotherapeutic agent, a radiotherapeutic agent, a hormonal therapeuticagent, an immunotherapeutic agent, etc.).

As a non-limiting example, the tyrosine kinase inhibitors describedherein can be co-administered with conventional chemotherapeutic agentsincluding platinum-based drugs (e.g., oxaliplatin, cisplatin,carboplatin, spiroplatin, iproplatin, satraplatin, etc.), alkylatingagents (e.g., cyclophosphamide, ifosfamide, chlorambucil, busulfan,melphalan, mechlorethamine, uramustine, thiotepa, nitrosoureas, etc.),anti-metabolites (e.g., 5-fluorouracil, azathioprine, methotrexate,leucovorin, capecitabine, cytarabine, floxuridine, fludarabine,gemcitabine, pemetrexed, raltitrexed, etc.), plant alkaloids (e.g.,vincristine, vinblastine, vinorelbine, vindesine, podophyllotoxin,paclitaxel, docetaxel, etc.), topoisomerase inhibitors (e.g.,irinotecan, topotecan, amsacrine, etoposide (VP16), etoposide phosphate,teniposide, etc.), antitumor antibiotics (e.g., doxorubicin, adriamycin,daunorubicin, epirubicin, actinomycin, bleomycin, mitomycin,mitoxantrone, plicamycin, etc.), pharmaceutically acceptable saltsthereof, stereoisomers thereof, derivatives thereof, analogs thereof,and combinations thereof.

The tyrosine kinase inhibitors described herein can also beco-administered with conventional hormonal therapaeutic agentsincluding, but not limited to, steroids (e.g., dexamethasone),finasteride, aromatase inhibitors, tamoxifen, and gonadotropin-releasinghormone agonists (GnRH) such as goserelin.

Additionally, the tyrosine kinase inhibitors described herein can beco-administered with conventional immunotherapeutic agents including,but not limited to, immunostimulants (e.g., Bacillus Calmette-Guérin(BCG), levamisole, interleukin-2, alpha-interferon, etc.), monoclonalantibodies (e.g., anti-CD20, anti-HER2, anti-CD52, anti-HLA-DR, andanti-VEGF monoclonal antibodies), immunotoxins (e.g., anti-CD33monoclonal antibody-calicheamicin conjugate, anti-CD22 monoclonalantibody-pseudomonas exotoxin conjugate, etc.), and radioimmunotherapy(e.g., anti-CD20 monoclonal antibody conjugated to ¹¹¹In, ⁹⁰Y, or ¹³¹I,etc.).

In a further embodiment, the tyrosine kinase inhibitors described hereincan be co-administered with conventional radiotherapeutic agentsincluding, but not limited to, radionuclides such as ⁷⁴Sc, ⁶⁴Cu, ⁶⁷Cu,⁸⁹Sr, ⁸⁶Y, ⁸⁷Y, ⁹⁰Y, ¹⁰⁵Rh, ¹¹¹Ag, ¹¹¹In, ^(117m)Sn, ¹⁴⁹Pm, ¹⁵³Sm,¹⁶⁶Ho, ¹⁷⁷Lu, ¹⁸⁶Re, ¹⁸⁸Re, ²¹¹At, and ²¹²Bi, optionally conjugated toantibodies directed against tumor antigens.

A therapeutically effective amount of a tyrosine kinase inhibitor may beadministered repeatedly, e.g., at least 2, 3, 4, 5, 6, 7, 8, or moretimes, or the dose may be administered by continuous infusion. The dosemay take the form of solid, semi-solid, lyophilized powder, or liquiddosage forms, such as, for example, tablets, pills, pellets, capsules,powders, solutions, suspensions, emulsions, suppositories, retentionenemas, creams, ointments, lotions, gels, aerosols, foams, or the like,preferably in unit dosage forms suitable for simple administration ofprecise dosages.

As used herein, the term “unit dosage form” refers to physicallydiscrete units suitable as unitary dosages for human subjects and othermammals, each unit containing a predetermined quantity of a tyrosinekinase inhibitor calculated to produce the desired onset, tolerability,and/or therapeutic effects, in association with a suitablepharmaceutical excipient (e.g., an ampoule). In addition, moreconcentrated dosage forms may be prepared, from which the more diluteunit dosage forms may then be produced. The more concentrated dosageforms thus will contain substantially more than, e.g., at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, or more times the amount of the tyrosine kinaseinhibitor.

Methods for preparing such dosage forms are known to those skilled inthe art (see, e.g., REMINGTON'S PHARMACEUTICAL SCIENCES, 18TH ED., MackPublishing Co., Easton, Pa. (1990)). The dosage forms typically includea conventional pharmaceutical carrier or excipient and may additionallyinclude other medicinal agents, carriers, adjuvants, diluents, tissuepermeation enhancers, solubilizers, and the like. Appropriate excipientscan be tailored to the particular dosage form and route ofadministration by methods well known in the art (see, e.g., REMINGTON'SPHARMACEUTICAL SCIENCES, supra).

Examples of suitable excipients include, but are not limited to,lactose, dextrose, sucrose, sorbitol, mannitol, starches, gum acacia,calcium phosphate, alginates, tragacanth, gelatin, calcium silicate,microcrystalline cellulose, polyvinylpyrrolidone, cellulose, water,saline, syrup, methylcellulose, ethylcellulose,hydroxypropylmethylcellulose, and polyacrylic acids such as Carbopols,e.g., Carbopol 941, Carbopol 980, Carbopol 981, etc. The dosage formscan additionally include lubricating agents such as talc, magnesiumstearate, and mineral oil; wetting agents; emulsifying agents;suspending agents; preserving agents such as methyl-, ethyl-, andpropyl-hydroxy-benzoates (i.e., the parabens); pH adjusting agents suchas inorganic and organic acids and bases; sweetening agents; andflavoring agents. The dosage forms may also comprise biodegradablepolymer beads, dextran, and cyclodextrin inclusion complexes.

For oral administration, the therapeutically effective dose can be inthe form of tablets, capsules, emulsions, suspensions, solutions,syrups, sprays, lozenges, powders, and sustained-release formulations.Suitable excipients for oral administration include pharmaceuticalgrades of mannitol, lactose, starch, magnesium stearate, sodiumsaccharine, talcum, cellulose, glucose, gelatin, sucrose, magnesiumcarbonate, and the like.

In some embodiments, the therapeutically effective dose takes the formof a pill, tablet, or capsule, and thus, the dosage form can contain,along with a tyrosine kinase inhibitor, any of the following: a diluentsuch as lactose, sucrose, dicalcium phosphate, and the like; adisintegrant such as starch or derivatives thereof; a lubricant such asmagnesium stearate and the like; and a binder such a starch, gum acacia,polyvinylpyrrolidone, gelatin, cellulose and derivatives thereof. Atyrosine kinase inhibitor can also be formulated into a suppositorydisposed, for example, in a polyethylene glycol (PEG) carrier.

Liquid dosage forms can be prepared by dissolving or dispersing atyrosine kinase inhibitor and optionally one or more pharmaceuticallyacceptable adjuvants in a carrier such as, for example, aqueous saline(e.g., 0.9% w/v sodium chloride), aqueous dextrose, glycerol, ethanol,and the like, to form a solution or suspension, e.g., for oral, topical,or intravenous administration. A tyrosine kinase inhibitor can also beformulated into a retention enema.

For topical administration, the therapeutically effective dose can be inthe form of emulsions, lotions, gels, foams, creams, jellies, solutions,suspensions, ointments, and transdermal patches. For administration byinhalation, a tyrosine kinase inhibitor can be delivered as a dry powderor in liquid form via a nebulizer. For parenteral administration, thetherapeutically effective dose can be in the form of sterile injectablesolutions and sterile packaged powders. Preferably, injectable solutionsare formulated at a pH of from about 4.5 to about 7.5.

The therapeutically effective dose can also be provided in a lyophilizedform. Such dosage forms may include a buffer, e.g., bicarbonate, forreconstitution prior to administration, or the buffer may be included inthe lyophilized dosage form for reconstitution with, e.g., water. Thelyophilized dosage form may further comprise a suitable vasoconstrictor,e.g., epinephrine. The lyophilized dosage form can be provided in asyringe, optionally packaged in combination with the buffer forreconstitution, such that the reconstituted dosage form can beimmediately administered to a subject.

A subject can also be monitored at periodic time intervals to assess theefficacy of a certain therapeutic regimen. For example, the levels ofcertain biomarkers can change based on the therapeutic effect of atreatment such as a tyrosine kinase inhibitor. The subject can bemonitored to assess response and understand the effects of certain drugsor treatments in an individualized approach. Additionally, subjects whoinitially respond to a tyrosine kinase inhibitor may become refractoryto the drug, indicating that these subjects have developed acquiredresistance to the drug. These subjects can be discontinued on theircurrent therapy and alternative treatments prescribed.

IX. Examples

The following examples are offered to illustrate, but not to limit, theclaimed invention.

Example 1 Algorithm for Predicting Response to Gefitinib (Iressa®)Therapy

This example illustrates an algorithm that was developed to predictresponse to gefitinib therapy. In particular, a representative panel ofgenetic, serological, and biochemical tests was performed on tumor,plasma, and/or serum samples to calculate a cumulative index value for asubject diagnosed with a solid tumor such as non-small cell lung cancer.In this example, representative index values predictive of gefitinibsensitivity were assigned a value of 1. However, one skilled in the artwill appreciate that the index values need not be integers. The finalindividual profile assessment can be presented as a cumulative indexvalue, which represents a summation of the representative index valuesdetermined for each biomarker. In certain instances, one or morebiomarkers can have a weighted representative index value.

As shown in FIG. 1, a subject diagnosed with a cancer such as non-smallcell lung cancer is first genotyped at a polymorphic site in EGFR todetermine the presence or absence of an EGFR activating mutation (100).The presence of an EGFR activating mutation indicates thatadministration of gefitinib should be recommended. Subjects who do nothave an EGFR activating mutation are then genotyped at a polymorphicsite in K-Ras to determine the presence or absence of a K-Ras activatingmutation (110). The presence of a K-Ras activating mutation indicatesthat administration of another tyrosine kinase inhibitor or analternative cancer therapy should be recommended. One skilled in the artwill appreciate that genotyping K-Ras can be performed at the same time,just prior to, or just after genotyping EGFR.

Various nucleic acid and/or protein profiles are then determined forthose subjects who do not have EGFR and K-Ras activating mutations usinga panel of biomarkers (120). For example, a gene copy number profile canbe determined by analyzing EGFR copy number and HER2 copy number; aprotein expression profile can be determined by measuring EGFRexpression, TGF-α expression, and PTEN expression; and a proteinactivation profile can be determined by assessing Erk (MAPK) activationand Akt activation. A cumulative index value (CIV) based upon the sum ofthe representative index values for each of these biomarkers can becalculated according to the following formula (130):

CIV=(2×EGFR copy number)+(2×EGFR expression)+Erk (MAPK) activation+Aktactivation+TGF-α expression+HER2 copy number+PTEN expression,

wherein

Representative Index Values Marker Sample Assay 0 1 EGFR copy numberTumor FISH No or low genomic gain High polysomy or gene amplificationTumor PCR ≦4-fold amplification >4-fold amplification EGFR expressionTumor IHC Negative to weak staining Moderate to strong staining Serum/ELISA ≦850 ng/ml >850 ng/ml Plasma Erk (MAPK) activation Tumor IHCNegative to weak staining Moderate to strong staining Akt activationTumor IHC Nuclear staining absent Nuclear staining present TGF-αexpression Tumor IHC Moderate to strong staining Negative to weakstaining Serum/ ELISA >15 pg/ml ≦15 pg/ml Plasma HER2 copy number TumorFISH No or low genomic gain High polysomy or gene amplification PTENexpression Tumor IHC Negative to weak staining Moderate to strongstaining

Here, a cumulative index value greater than or equal to an index cut-offvalue of 4 is predictive of gefitinib sensitivity or an increasedlikelihood of responding to gefitinib (140). Administration of gefitinibshould be recommended. However, a cumulative index value less than anindex cut-off value of 4 is predictive of gefitinib insensitivity or adecreased likelihood of responding to gefitinib. Administration ofanother tyrosine kinase inhibitor or an alternative cancer therapyshould be recommended.

Example 2 Algorithm for Predicting Response to Sunitinib (Sutent®)Therapy

This example illustrates an algorithm that was developed to predictresponse to sunitinib therapy. In particular, a representative panel ofgenetic, serological, and biochemical tests was performed on tumor,plasma, and/or serum samples to calculate a cumulative index value for asubject diagnosed with a solid tumor such as a gastrointestinal stromaltumor or renal cell carcinoma. In this example, representative indexvalues predictive of sunitinib sensitivity were assigned a value of 1.However, one skilled in the art will appreciate that the index valuesneed not be integers. The final individual profile assessment can bepresented as a cumulative index value, which represents a summation ofthe representative index values determined for each biomarker. Incertain instances, one or more biomarkers can have a weightedrepresentative index value.

As shown in FIG. 2, a subject diagnosed with a cancer such as agastrointestinal stromal tumor or renal cell carcinoma is firstgenotyped at a polymorphic site in c-KIT, PDGFR, and/or VEGFR todetermine the presence or absence of activating mutations in these genes(200). In addition, VEGFR and/or PDGFR expression is measured in a serumor plasma sample and c-KIT expression is determined byimmunohistochemistry (IHC). The presence of a c-KIT, PDGFR, and/or VEGFRactivating mutation, in combination with VEGFR and/or PDGFRoverexpression and c-KIT overexpression, indicates that administrationof sunitinib should be recommended. Subjects negative for thesebiomarkers are then genotyped at a polymorphic site in K-Ras todetermine the presence or absence of a K-Ras activating mutation (210).The presence of a K-Ras activating mutation indicates thatadministration of another tyrosine kinase inhibitor or an alternativecancer therapy should be recommended. One skilled in the art willappreciate that genotyping K-Ras can be performed at the same time, justprior to, or just after genotyping c-KIT, PDGFR, and/or VEGFR.

Various nucleic acid and/or protein profiles are then determined forthose subjects who are negative for c-KIT, PDGFR, VEGFR, and K-Rasactivating mutations and do not overexpress VEGFR, PDGFR, and c-KITusing a panel of biomarkers (220). For example, a protein expressionprofile can be determined by measuring PTEN expression; and a proteinactivation profile can be determined by assessing Erk (MAPK) activationand Akt activation. A cumulative index value (CIV) based upon the sum ofthe representative index values for each of these biomarkers can becalculated according to the following formula (230):

CIV=Erk (MAPK) activation+Akt activation+PTEN expression, wherein

Representative Index Values Marker Sample Assay 0 1 Erk (MAPK) Tumor IHCNegative to weak Moderate to strong activation staining staining AktTumor IHC Nuclear staining Nuclear staining activation absent presentPTEN Tumor IHC Negative to weak Moderate to strong expression stainingstaining

Here, a cumulative index value greater than or equal to an index cut-offvalue of 2 is predictive of sunitinib sensitivity or an increasedlikelihood of responding to sunitinib (240). Administration of sunitinibshould be recommended. However, a cumulative index value less than anindex cut-off value of 2 is predictive of sunitinib insensitivity or adecreased likelihood of responding to sunitinib. Administration ofanother tyrosine kinase inhibitor or an alternative cancer therapyshould be recommended.

Example 3 Biomarker Analysis in Fractionated Whole Blood

This example illustrates the use of fractionated whole blood fordetermining a spectrum of profiles including a genotypic profile, genecopy number profile, gene expression profile, DNA methylation profile,protein expression profile, protein activation profile, and combinationsthereof. Whole blood which has been separated into its liquid andcellular components can also be used for determining the localization ofproteinaceous biomarkers of interest, the morphology of cells ofinterest, and the number of circulating tumor and/or endothelial cellsin a subject diagnosed with a solid tumor.

Circulating tumor and/or endothelial cells can act as a surrogate forbiomarker analysis of the primary or metastatic tumor. In addition toreleasing intact viable cells into the circulation, tumors also releasefreely circulating DNA, RNA, and shed proteins at levels that can beanalyzed with current technologies. By segregating a whole blood sampleinto its fluid and cellular components, an entire spectrum of biomarkerscan be analyzed using a single sample. As a result, all of thebiomarkers in Examples 1 and 2 that are typically analyzed in tumortissue can alternatively be analyzed in a fractional component of wholeblood. FIG. 3 shows a flow diagram illustrating the analyses applicablefor each whole blood fraction.

Whole blood is typically fractionated into a plasma or serum componentand a cellular component using an art-recognized method such ascentrifugation. As a non-limiting example, whole blood can be collectedaccording to standard procedures in tubes containing an anticoagulantsuch as EDTA and fractionated by centrifuging at about 1500-2000×g forabout 10-15 min at room temperature. This protocol is useful forseparating whole blood into an upper plasma layer (i.e., plasmafraction) and a lower cellular layer (i.e., cell pellet fraction).

As shown in FIG. 3, DNA, RNA, and proteins secreted by tumor cells canbe analyzed in the plasma fraction of whole blood. For example, DNA canbe isolated from the plasma fraction using any method known in the artand a mutational analysis performed to determine the genotype of genessuch as tyrosine kinase genes and/or a small GTPase genes. The level ofDNA methylation in genomic regulatory sequences can also be detected inisolated DNA using any of the techniques described above. In addition,RNA can be isolated from the plasma fraction using any method known inthe art and a gene expression analysis can be performed to determine thelevel of expression of cancer and/or tissue-specific genes using any ofthe above-described techniques. Moreover, the expression level of one ormore proteinaceous biomarkers such as tyrosine kinases, growth factors,and/or tumor suppressors can be determined in a plasma fraction usingimmunoassays or other art-recognized techniques as described above.

FIG. 3 also shows that circulating tumor cells (CTCs) and circulatingendothelial cells (CECs) can be analyzed in the cell pellet fraction ofwhole blood. Since CTCs and CECs are relatively rare, they can first beenriched using an immunomagnetic assay available from, e.g., ImmuniconCorp. (Huntingdon Valley, Pa.), or any other magnetic-activated cellseparation technique known in the art. A negative selection can also beperformed to remove red blood cells and white blood cells from the cellpellet fraction. The enriched CTCs and CECs can be analyzed using any ofa variety of microscopic techniques including, for example, in situhybridization, immunohistochemistry, and immunofluorescence, todetermine cell surface protein expression and/or localization, cellmorphology, and CTC/CEC number. Proximity-based assays such asscintillation proximity assays (see, e.g., McDonald et al., Anal,Biochem., 268:318-329 (1999), fluorescence polarization assays (see,e.g., Scott et al., Anal. Biochem., 316:82-91 (2003)), and luminescentproximity assays (see, e.g., U.S. Patent Publication No. 20060063219),as well as any of the techniques described above, can be used todetermine the phosphorylation state of at least one tyrosine kinasesignaling component in CTCs and CECs.

Example 4 Prediction of Gefitinib-Sensitive Cell Lines Using ArtificialIntelligence

This example illustrates that the use of learning statistical classifiersystems to combine the information from disparate sample sets results ingreater diagnostic power than each set provides alone.

Samples

A nucleic acid and/or protein profile of one or more biomarkers in a setof cancer cell lines was obtained to generate a data file of inputvalues for use in an algorithm to predict whether a particular cell linewould be responsive to treatment with gefitinib (Iressa®). As anon-limiting example, the level of EGFR family kinase expression (i.e.,HER1-4) and Akt phosphorylation in the breast cancer cell linesdescribed by Moasser et al. (Cancer Res., 61:7184-7188 (2001)) was usedto generate a data file of input values for statistical analysis (Table1). The breast cancer cell lines were BT474, MDA-MB-361, ZR75, T47D,MDA-MIB-231, SkBr3, MDA-MB-453, A431, MDA-MB-468, A549, PC3, SkOv3,DU145, MCF-7, Colo205, T24, and DU4475. One skilled in the art willappreciate that the nucleic acid and/or protein profile of biomarkers ofinterest can be obtained either prospectively or retrospectively fromone or more patient sample sets and used in the algorithms describedherein for predicting whether a particular type of tumor would beresponsive to gefitinib therapy.

TABLE 1 Data file of input values used in algorithmic analysis of breastcancer cell lines. Cell Line HER1 HER2 HER3 HER4 IC₅₀ CATAG Akt SUB1SUB2 BT474 1 5000 150 10 0.8 1 10 Train Train MDA-MB-361 1 1000 100 2 81 20 Train Test ZR75 0 100 1 3 16 0 85 Train Train T47D 1 50 200 1000 120 85 Train Test MDA-MIB-231 100 30 0 1 15 0 70 Train Train SkBr3 50 500050 3 1 1 2 Train Test MDA-MB-453 0.5 1000 200 5 7 1 40 Train Test A4312000 120 100 0 1 1 2 Test Test MDA-MB-468 2000 0 100 0 13.5 0 90 TrainTrain A549 75 90 2 0 12 0 85 Test Train PC3 75 30 0 0 14 0 55 Test TrainSkOv3 200 2000 1 20 2.5 1 40 Test Train DU145 80 40 1 0 7 1 35 Test TestMCF-7 1 40 200 200 15 0 70 Train Train Co1o205 1 200 5 5 12 0 105 TestTest T24 50 90 0 0 18 0 90 Test Train DU4475 0.5 0 1 1 10 0 105 TestTest The densities of the HER1, HER2, HER3, and HER4 bands in theWestern blot from Figure 1 of Moasser et al. were estimated by naked eyeobservation and given a relative value ranging from 0 to 5000. The levelof Akt activity for each cell line was determined using the data fromFIG. 3 of Moasser et al. at a gefitinib concentration of 10 μM. An IC₅₀< 9 μM was considered gefitinib-sensitive (“CATAG” = 1).

Statistical Analyses

In this study, two different learning statistical classifiers were used(e.g., random forests and artificial neural networks) to predictsensitivity of the cell lines to gefitinib. These learning statisticalclassifiers use multivariate statistical methods like, for example,multilayer perceptrons with feed-forward backpropagation that can adaptto complex data and make decisions based strictly on the data presented,without the constraints of regular statistical classifiers.

Random Forests

Each breast cancer cell line sample was randomly selected for randomforest (RF) prediction. Out-of-the-bag data was used for testing.Multiple RF models using commercially available software (SalfordSystems; San Diego, Calif.) were created and analyzed for accuracy ofprediction using the test cohort. The best predictive RF models wereselected and tested for accuracy of prediction using data from thevalidation cohort. The success of the RF prediction is shown in Table 2.Table 3 shows a ranking of the importance of the variables.

TABLE 2 Random forest prediction success. Sensitive Insensitive ActualCell Line Total Cases Percent Correct N = 7 N = 10 Sensitive 7 100 7 0Insensitive 10 100 0 10

TABLE 3 Random forest variable importance. Variable Score Akt 100.00|||||||||||||||||||||||||||||||||||||||||| HER2 30.03 |||||||||||| HER33.58 | HER1 0.00 HER4 0.00

Artificial Neural Networks

Each breast cancer cell line sample was randomly selected for neuralnetwork prediction, with a total of 9 for training and 8 for validation.Different samples were used for training, testing, and for validationpurposes. The Intelligent Problem Solver module of the neural networkssoftware package (Statistica; StatSoft, Inc.; Tulsa, Okla.) was used tocreate artificial neural network (ANN) models in a feed-forward,backpropagation, and classification mode with the training cohort.Linear, multi-layer perceptron (MLP), and probabilistic neural networks(PNN) models are shown in Table 4. The best models were selected basedon the lowest error of prediction on the test dataset.

TABLE 4 Neural network prediction accuracy. Model Subset Sensitive*Insensitive* Linear 1 3/3 5/5 MLP 1 3/3 5/5 PNN 2 4/5 3/3 Linear 3 4/44/4 MLP 4 2/2 6/6 *= Predicted numbers/observed numbers.

All publications and patent applications cited in this specification areherein incorporated by reference as if each individual publication orpatent application were specifically and individually indicated to beincorporated by reference. Although the foregoing invention has beendescribed in some detail by way of illustration and example for purposesof clarity of understanding, it will be readily apparent to those ofordinary skill in the art in light of the teachings of this inventionthat certain changes and modifications may be made thereto withoutdeparting from the spirit or scope of the appended claims.

1-75. (canceled)
 76. An assay method for predicting the response of asubject diagnosed with non-small cell lung cancer to treatment withgefitinib (Iressa®), said method comprising: (a) analyzing a sampleobtained from said subject to determine the presence or absence of anactivating mutation in the EGFR gene in said sample; (b) analyzing saidsample to determine the presence or absence of an activating mutation inthe K-Ras gene when said activating mutation in the EGFR gene is absent;and (c) predicting an increased likelihood that said subject willrespond to treatment with gefitinib when said activating mutation in theEGFR gene is present, and predicting a decreased likelihood that saidsubject will respond to treatment with gefitinib when said activatingmutation in the K-Ras gene is present.
 77. The method of claim 76,wherein said activating mutation in the EGFR gene comprises a deletion,an insertion, or a single nucleotide substitution in the tyrosine kinasedomain of the EGFR gene.
 78. The method of claim 76, wherein saidactivating mutation in the K-Ras gene results in a substitution in theK-Ras amino acid sequence selected from the group consisting of acysteine for glycine at position 12 (G12C), a cysteine for glycine atposition 13 (G13C), an aspartic acid for glycine at position 12 (G12D),a serine for glycine at position 12 (G12S), and a valine for glycine atposition 12 (G12V).
 79. The method of claim 76, wherein said sample isselected from the group consisting of whole blood, serum, plasma, urine,nipple aspirate, lymph, saliva, fine needle aspirate, tumor tissue, andcombinations thereof.
 80. The method of claim 79, wherein said samplecomprises circulating tumor cells, circulating endothelial cells,circulating endothelial progenitor cells, cancer stem cells, orcombinations thereof.
 81. The method of claim 76, wherein said methodfurther comprises sending the results from said prediction to aclinician.
 82. The method of claim 76, further comprising recommendingthe administration of gefitinib to said subject when said subject ispredicted to have an increased likelihood of responding to treatmentwith gefitinib.
 83. The method of claim 76, further comprisingrecommending the administration of another tyrosine kinase inhibitor oran alternative cancer therapy to said subject when said subject ispredicted to have a decreased likelihood of responding to treatment withgefitinib.
 84. The method of claim 76, further comprising analyzing saidsample to determine the EGFR and HER2 copy number, the level of EGFR,TGF-α, and PTEN protein expression, and the presence or absence of Erk(MAPK) and Akt activation when said activating mutation in the K-Rasgene is absent.
 85. The method of claim 84, wherein the EGFR and HER2copy number, the level of EGFR, TGF-α, and PTEN protein expression, andthe presence or absence of Erk (MAPK) and Akt activation are eachassigned an index value.
 86. The method of claim 85, wherein acumulative index value (CIV) is calculated by summing said index valuesassigned to the EGFR and HER2 copy number, the level of EGFR, TGF-α, andPTEN protein expression, and the presence or absence of Erk (MAPK) andAkt activation, and wherein said index values assigned to the EGFR copynumber and the level of EGFR protein expression are multiplied by afactor of
 2. 87. The method of claim 86, further comprising comparingsaid CIV to an index cutoff value.
 88. The method of claim 87, whereinsaid subject is predicted to have an increased likelihood of respondingto treatment with gefitinib when said CIV is greater than or equal tosaid index cut-off value.
 89. The method of claim 88, further comprisingrecommending the administration of gefitinib to said subject.
 90. Themethod of claim 87, wherein said subject is predicted to have adecreased likelihood of responding to treatment with gefitinib when saidCIV is less than said index cut-off value.
 91. The method of claim 90,further comprising recommending the administration of another tyrosinekinase inhibitor or an alternative cancer therapy to said subject. 92.The method of claim 76, wherein the presence or absence of saidactivating mutation in the EGFR or K-Ras genes is determined by thepolymerase chain reaction (PCR).
 93. The method of claim 84, wherein theEGFR and HER2 copy number is determined by fluorescence in situhybridization (FISH) or PCR.
 94. The method of claim 84, wherein thelevel of EGFR, TGF-α, and PTEN protein expression is determined byimmunohistochemistry (IHC) or an immunoassay.
 95. The method of claim84, wherein the presence or absence of Erk (MAPK) and Akt activation isdetermined by IHC.